SAA-C03 AWS Certified Solutions Architect - Associate (SAA-C03) Questions and Answers

Amazon Macieis purpose-built for detecting PII in S3.

Option Auses EventBridge to filter SensitiveData findings and notify via SNS, meeting the requirements.

Options B and Dinvolve GuardDuty, which is not designed for PII detection.

Option Cuses SQS, which is less suitable for immediate notifications.

Amazon Kinesis Data Firehose (now known as Amazon Data Firehose) supports near-real-time streaming, with built-in support to:

Invoke AWS Lambda functions for transformation.

Store data directly into Amazon S3 in desired formats like Apache Parquet.

“You can use Data Firehose to transform the data before delivering it, by invoking a Lambda function. You can convert to formats such as JSON or Apache Parquet before storing it into S3.”

— Amazon Data Firehose Developer Guide

Why not the others?

B: Kinesis Data Streams requires more operational overhead than Firehose.

C: DataSync is for file movement—not stream processing.

D: SQS isn’t designed for streaming and transformation pipelines.

The combination of these solutions provides an efficient and automated way to migrate data while preserving metadata and ensuring cleanup:

Create a new S3 bucket with versioning enabled(Option A) to preserve object metadata like version IDs during migration.

UseS3 batch operations(Option B) to efficiently copy data from specific prefixes in the source bucket to the destination bucket, ensuring minimal overhead.

Use an S3 Lifecycle policy(Option F) to automatically delete the data from the source bucket after it has been migrated, reducing manual intervention.

Option C (CopyObject API): This approach would require more manual scripting and effort.

Option D (Same-Region Replication): SRR is designed for ongoing replication, not for one-time migrations.

Option E (DataSync): DataSync adds more complexity than necessary for this task.

AWS References:

S3 Batch Operations

S3 Lifecycle Policies

Regulatory compliance often requires immutability, meaning data cannot be altered or deleted by any user, including administrators, during a retention period. Amazon S3 Object Lock in compliance mode is specifically designed for this requirement.

Option B enforces a write-once-read-many (WORM) model. Once compliance mode is enabled and a retention period is set, no one—including root users—can delete or overwrite the objects until the retention expires. This provides the strongest level of protection and satisfies strict regulatory standards.

Option A (governance mode) allows privileged users to override the lock, which may not satisfy regulatory mandates. Option C does not prevent deletion or modification before retention expiration. Option D addresses storage cost, not immutability.

Therefore, B is the correct solution for regulatory-grade data protection.

Durable storage of incoming orders with buffering and ability to handle surges is exactly what Amazon SQS is designed for. SQS provides highly durable, scalable queues that decouple producers from consumers.

Centrally tracking workflow steps is a core use case of AWS Step Functions, which gives a visual workflow and state machine, tracks the state of each order, and can orchestrate calls to multiple microservices (in this case, Lambda functions).

Combining SQS + Step Functions + Lambda gives:

Durable queueing for orders (SQS).

Loose coupling and surge handling (SQS decoupling + auto-scaling Lambda).

Central orchestration and tracking of order-processing steps (Step Functions).

Why the other options are not correct:

A: SNS is a pub/sub service, not a durable work queue, and is not designed for “store-and-retry until processed” workloads in the same way SQS is.

C: SQS + EventBridge provides decoupling but no central, stateful workflow tracking; EventBridge is event routing, not workflow orchestration.

D: SNS + EventBridge still lacks durable order storage and explicit centralized workflow/state tracking.

To address the high CPU utilization on the EC2 instance and the degraded performance of Amazon RDS for read requests, the solution involves two key actions: resizing the EC2 instance and leveraging Amazon RDS read replicas.

Resizing the EC2 Instance: The first step is to resize the EC2 instance to a type with more CPU capacity to handle the higher computational demands during peak usage times. This helps to alleviate the immediate pressure on the CPU.

Auto Scaling Group with a Size of 1: Although the application can only run on a single EC2 instance due to its monolithic nature, creating an Auto Scaling group with a minimum and maximum size of 1 ensures that the instance is automatically restarted or replaced in case of failure, maintaining high availability.

RDS Read Replica: Configuring an RDS read replica allows the application to offload read requests to a separate instance, thus reducing the load on the primary RDS instance. This improves the performance of read operations, which were previously bottlenecked due to the high CPU usage on the EC2 instance.

Why Not Other Options?:

Option B: Redirecting all traffic to the RDS read replica is not recommended because replicas are meant for read traffic only, not for write operations. This could lead to data consistency issues.

Option C: Increasing the RDS instance type capacity helps, but it doesn’t address the high CPU usage on the EC2 instance, nor does it provide a solution for scaling reads.

Option D: While resizing both the EC2 and RDS instances increases their capacities, it doesn’t address the specific need to offload read traffic from the primary RDS instance.

AWS References:

Amazon RDS Read Replicas- Explains how to create and use read replicas to offload read traffic from the primary database instance.

Resizing Your EC2 Instance- Guidance on resizing EC2 instances to meet workload demands.

Amazon S3 Intelligent-Tiering automatically moves objects between two access tiers based on changing access patterns. Objects not accessed for 30 days move to a lower-cost tier, but are still immediately available with millisecond retrieval. If objects become frequently accessed again, they are moved back to the frequent access tier. There are no retrieval charges and no impact on availability or performance. This storage class is specifically designed for unpredictable access patterns and cost optimization, requiring minimal management.

AWS Documentation Extract:

" S3 Intelligent-Tiering is the only storage class that automatically moves data between frequent and infrequent access tiers when access patterns change, with no retrieval charges and no impact on performance. It is designed to optimize costs automatically when data access patterns are unpredictable. "

(Source: Amazon S3 documentation, Intelligent-Tiering storage class)

B: Glacier Deep Archive is for archival, not for low-latency millisecond access.

C: EFS is not optimized for object storage or global CDN distribution.

D: Cache-Control only affects CloudFront or browser caching, not S3 storage cost.

AWS documentation states that workloads running 24/7 should use Reserved Instances or Savings Plans for the lowest cost. Therefore, the frontend nodes, which always run, should use Reserved Instances.

The backend nodes process asynchronous, restartable jobs, which makes them ideal for EC2 Spot Instances, the most cost-effective compute option for interruption-tolerant workloads.

Fargate (Options A and D) is significantly more expensive for large compute usage. Spot Instances cannot be used for critical frontend nodes (Option C).

=====================================================

To grant the ECS application the least privilege, you create an IAM policy that explicitly lists the ARNs of the specific S3 buckets the task should access. You then attach this policy to the task role (not the instance role), ensuring only that task has the necessary permissions. This approach avoids granting permissions to other EC2 instances or services and restricts access strictly to the required buckets.

AWS Documentation Extract:

" Attach an IAM policy granting access to specific resources to the ECS task role, not to the instance role, to ensure that only the container has access according to the principle of least privilege. "

" Use explicit ARNs to control access only to specified S3 buckets. "

(Source: Amazon ECS documentation, IAM Roles for Tasks)

A: Tag-based access control for S3 buckets is possible but less direct and commonly used for identity-based access.

C: Attaching the policy to the instance role could grant unintended permissions to all containers or services running on the instance.

D: Wildcard ARNs grant broader access than necessary.

Amazon S3 Intelligent-Tiering: This storage class is designed to optimize costs by automatically moving data between two access tiers (frequent and infrequent) when access patterns change. It provides cost savings without performance impact or operational overhead.

S3 Lifecycle Rules: By creating an S3 Lifecycle rule, the company can automatically transition objects to the Intelligent-Tiering storage class. This eliminates the need for manual intervention and ensures that objects are moved to the most cost-effective storage tier based on their access patterns.

Operational Efficiency: Intelligent-Tiering requires no additional management and delivers immediate availability for frequently accessed objects. This makes it the most operationally efficient solution for the given requirements.

The requirement is to scan container images for CVEs with the fewest changes to existing ECS workloads. Amazon ECS integrates natively with Amazon Elastic Container Registry (ECR), making ECR the natural place to implement vulnerability scanning without modifying the orchestration platform.

Option A meets the requirement most efficiently. ECR provides built-in vulnerability scanning that can be enabled using scan on push, ensuring that every new image uploaded to the repository is automatically scanned for known CVEs. This capability requires no changes to ECS task definitions beyond referencing ECR images, which is typically already the case. Scan results are maintained by AWS and can be reviewed centrally, providing ongoing security visibility.

Option C introduces a major architectural change by migrating to EKS, which violates the “fewest changes” requirement. Options B and D misuse services: Amazon Macie is designed for sensitive data discovery in S3, not container image vulnerability scanning, and storing images in S3 breaks container-native workflows. Lambda-driven Inspector scans for container images are not required when ECR already provides integrated scanning.

Therefore, A is the optimal solution because it leverages native ECS–ECR integration, requires minimal operational changes, and provides automated CVE scanning aligned with AWS container security best practices.

EBS Encryption:

Default EBS Encryption: Can be enabled for new EBS volumes.

Use of AWS KMS: Specify AWS KMS keys to handle encryption and decryption of data transparently.

Amazon RDS Encryption:

RDS Encryption: Encrypts the underlying storage for RDS instances using AWS KMS.

Configuration: Enable encryption when creating the RDS instance or modify an existing instance to enable encryption.

Least Amount of Changes:

Both EBS and RDS support seamless encryption with AWS KMS, requiring minimal changes to the existing infrastructure.

Enables compliance with regulatory requirements without modifying the application.

Operational Efficiency: Using AWS KMS for both EBS and RDS ensures a consistent, managed approach to encryption, simplifying key management and enhancing security.

AWS documentation states that when Lambda processes events, a dead-letter queue (DLQ) using Amazon SQS is the correct mechanism to capture failed invocations for later reprocessing.

SQS provides durable buffering and decoupling, allowing records to be retried safely without loss.

Kinesis Firehose (Option B) does not support SQL replays or arbitrary replay control. SNS (Option D) is not a durable store. OpenSearch (Option C) is not intended for durable retry queues.

=====================================================

Comprehensive and Detailed Step-by-Step Explanation:

To accurately charge customers based on their monthly data download usage, the following solution is recommended:

Structured Logging Configuration:

Action:Ensure that the AWS Transfer for SFTP server is configured to log user activity, including details about file downloads, to Amazon S3 in a structured format.

Implementation:Utilize AWS Transfer Family ' s structured logging feature to capture detailed information about user sessions, including actions performed and data transferred.

docs.aws.amazon.com

Justification:Structured logs provide comprehensive data necessary for analyzing customer-specific download activities.

Data Analysis with Amazon Athena:

Action:Use Amazon Athena to run SQL queries on the structured log data stored in the S3 bucket to calculate the amount of data each customer has downloaded.

Implementation:

a.Define a Schema:Create a table in Athena that maps to the structure of your log files. This involves specifying the format of the logs and the location in S3.

b.Query Data:Write SQL queries to sum the total bytes downloaded by each customer over the billing period. This can be achieved by filtering logs based on user identifiers and summing the data transfer amounts.

Justification:Athena allows for efficient querying

Same scenario as above—cost-effective cold start reduction for Java Lambdas is best achieved by using Lambda SnapStart with the correct code structure. Unique IDs and similar non-deterministic logic should be in the handler or in a pre-snapshot hook. SnapStart is supported only for published versions. This combination offers cold start performance at a lower cost than provisioned concurrency.

AWS Documentation Extract:

" When using Lambda SnapStart for Java, ensure non-deterministic initialization code, such as unique ID generators, is executed inside the handler or in a pre-snapshot hook. SnapStart is supported only on published versions. "

(Source: AWS Lambda documentation, SnapStart for Java)

For applications requiring high network throughput and low latency between EC2 instances, AWS recommends using a Cluster Placement Group:

Cluster Placement Group: Packs instances close together inside an Availability Zone, enabling workloads to achieve the low-latency network performance necessary for tightly-coupled node-to-node communication.

Enhanced Networking: Selecting instance types that support enhanced networking provides higher bandwidth, higher packet-per-second (PPS) performance, and consistently lower inter-instance latencies.

By combining a Cluster Placement Group with enhanced networking-enabled instance types, the company can meet the stringent performance requirements of its real-time industrial application.

The requirement is for immediate processing of uploaded videos and prompt notification to users. According to AWS best practices for event-driven architectures, using S3 event notifications to trigger a Lambda function upon an object creation (upload) is the optimal solution for real-time processing. Lambda can process the file as soon as it is uploaded, ensuring low latency. Once processing is complete, Lambda can save the processed file back to S3 and use Amazon SNS to notify the user.

This approach uses managed services with minimal operational overhead, is scalable, and ensures event-driven processing with instant user feedback. AWS Amplify primarily facilitates application development and hosting but does not natively provide direct push notification support for this backend workflow; instead, SNS is designed for such notification scenarios.

Reference Extract from AWS Documentation / Study Guide:

" Amazon S3 can publish events to AWS Lambda when objects are created. AWS Lambda runs code in response to events and can interact with other AWS services. Amazon SNS is a flexible, fully managed pub/sub messaging and mobile notifications service for coordinating the delivery of messages to subscribing endpoints and clients. "

Source: AWS Certified Solutions Architect – Official Study Guide, Event-driven Architectures section; AWS Lambda Developer Guide (S3 triggers); Amazon SNS User Guide.

Spot Instances can be interrupted when AWS needs the capacity back. To reduce interruptions and improve availability, AWS provides the capacity-optimized allocation strategy.

Capacity-optimized strategy launches Spot Instances from the most available Spot capacity pools instead of the lowest-priced ones, reducing interruption rates.

By adding multiple instance types (e.g., using Instance Type Flexibility), the Auto Scaling group can launch instances in a broader set of pools, improving the chance that capacity is available.

Scheduled scaling actions combined with a diverse set of instances under the capacity-optimized strategy ensure higher resilience and better timing for instance launches.

This approach directly supports the Resiliency design principle in the AWS Well-Architected Framework.

???? References:

Best Practices for EC2 Spot Instances

Capacity-Optimized Allocation Strategy

The correct answer is D because the company needs to authenticate users from an on-premises LDAP directory to the AWS Management Console , but the directory is not SAML-compatible . In this scenario, the AWS-recommended pattern is to use a custom identity broker that authenticates users against the existing LDAP directory and then requests temporary AWS credentials from AWS Security Token Service (AWS STS) . This enables federated access to AWS without creating long-term IAM user credentials for each person.

A custom identity broker serves as the intermediary between the non-SAML directory and AWS. After validating a user with LDAP, the broker can call AWS STS to issue short-lived credentials or console sign-in access. This approach preserves the use of the company’s existing identity source while meeting AWS security best practices for temporary credentials and centralized identity management.

Option A is incorrect because AWS IAM Identity Center typically relies on supported identity sources and federation methods, and the question explicitly states that the LDAP directory is not SAML-compatible. Option B is incorrect because IAM policies do not integrate directly into LDAP as an authentication mechanism. Option C is incorrect because rotating IAM credentials tied to LDAP changes still relies on long-term credentials and does not provide a proper federated sign-in model.

AWS federation guidance supports the use of a custom identity broker when an organization has a non-SAML-compatible identity system. Therefore, the best solution is to develop an on-premises custom identity broker that uses AWS STS to obtain short-lived credentials for AWS Management Console access.

Aurora global database is specifically designed for cross-Region disaster recovery and low-latency global reads.

It provides:

Physical storage-level replication between Regions, typically with lag of under 1 second, easily satisfying RPO 1 minute.

Fast cross-Region failover capabilities, often within minutes or less, which meets the RTO 5 minutes requirement.

You configure the primary Region as the writer cluster and additional Regions (such as us-west-1) as secondary clusters with reader endpoints. In a disaster, you promote the secondary to be the new writer.

Why others are not correct:

A: Standard cross-Region read replicas for Aurora PostgreSQL do not provide automatic multi-Region failover with the same guarantees as Aurora global database and may not meet strict RTO/RPO requirements.

C: Logical replication and custom EventBridge failover is complex, error-prone, and higher overhead compared to the managed global database feature.

D: Restoring from snapshots plus cross-Region replication is too slow to meet RTO 5 minutes and certainly cannot maintain RPO 1 minute.

A. Aurora MySQL:Handles OLTP workloads efficiently with built-in replication and auto-scaling capabilities.

B. EC2 with MySQL:Requires heavy manual maintenance and does not scale seamlessly.

C. RDS for MySQL:Limited in auto-scaling compared to Aurora.

D. Redshift:Primarily for OLAP, not suitable for OLTP workloads.

The most cost-effective and scalable solution for generating thumbnails when photos are uploaded to an S3 bucket is to useS3 event notificationsto trigger anAWS Lambda function. This approach avoids the need for a long-running EC2 instance or EMR cluster, making it highly cost-effective because Lambda only charges for the time it takes to process each event.

S3 Event Notifications: Automatically triggers the Lambda function when a new photo is uploaded to the S3 bucket.

AWS Lambda: A serverless compute service that scales automatically and only charges for execution time, which makes it the most economical choice when dealing with periodic events like photo uploads.

The Lambda function can generate the thumbnail and upload it to a second S3 bucket, fulfilling the requirement efficiently.

Option AandOption B(EMR or EC2 with scheduled scripts)**: These are less cost-effective as they involve continuously running infrastructure, which incurs unnecessary costs.

Option D (S3 Storage Lens): S3 Storage Lens is a tool for storage analytics and is not designed for event-based photo processing.

AWS References:

Amazon S3 Event Notifications

AWS Lambda Pricing

AWS Secrets Manager is the recommended service for managing and automatically rotating database credentials. It integrates natively with Amazon RDS (including PostgreSQL), supports built-in rotation functionality, and requires minimal setup.

Secrets Manager also supports versioning and auditing, which enhances operational excellence and security. Parameter Store does not natively support credential rotation. AWS KMS manages key encryption—not application secrets—so it is not applicable here.

BothAmazon KinesisandSQS FIFOqueues ensure the sequential processing of messages. By using the payment ID as the partition key in Kinesis or as the message group in the SQS FIFOqueue, messages are processed in order. Both solutions also allow for long-term retention (up to 10 days) of messages, making them suitable for this payment processing use case.

Option A (DynamoDB): DynamoDB does not guarantee message ordering for real-time processing.

Option C (ElastiCache): ElastiCache is for caching, not suitable for sequential message processing.

Option D (Standard SQS queue): A standard SQS queue does not guarantee ordering of messages.

AWS References:

Amazon Kinesis

Amazon SQS FIFO Queues

To track costs associated with different teams within a single AWS account, the company should implement user-defined cost allocation tags.

Tagging Resources with CostCenter: Assign the CostCenter tag to all resources, specifying the appropriate value for each team. This practice enables the organization to categorize and track AWS costs effectively.

Activating the CostCenter Tag: After tagging resources, activate the CostCenter tag in the AWS Billing and Cost Management console. Activation is necessary for the tags to appear in cost allocation reports and AWS Cost Explorer.

By following these steps, the company can generate detailed billing reports that break down costs by team, facilitating better cost management and accountability.

CloudTrail log file validation ensures that the log files have not been altered or deleted after delivery, providing an immutable audit log. Using KMS-managed encryption keys for CloudTrail log files adds another layer of data security, and AWS Config can monitor compliance to ensure this security is always enforced.

Reference Extract:

" CloudTrail log file validation provides assurance about the integrity of CloudTrail logs. Using SSE-KMS encryption and monitoring with AWS Config helps secure and audit logs for compliance. "

Source: AWS Certified Solutions Architect – Official Study Guide, CloudTrail Security section.

Converting the existing DynamoDB table to aglobal tableprovides active-active replication and low-latency reads and writes in both Regions. DynamoDB global tables are specifically designed for multi-Region and multi-active use cases.

Option A:GSIs do not provide multi-Region replication or active-active capabilities.

Option B and D:Using DynamoDB Streams and custom replication is less operationally efficient than global tables and introduces additional complexity.

AWS Documentation References:

DynamoDB Global Tables

AWS Secrets Manager is a fully managed service specifically designed to securely store and automatically rotate database credentials, API keys, and other secrets. Secrets Manager provides built-in integration with Amazon RDS for automatic credential rotation on a configurable schedule without requiring downtime. It also manages the secure distribution of the credentials to authorized services, such as your web servers, using IAM policies. Manual solutions (S3, files, cron jobs) do not provide the same level of automation, audit, or security.

Reference Extract from AWS Documentation / Study Guide:

" AWS Secrets Manager enables you to rotate, manage, and retrieve database credentials securely. It supports automatic rotation of secrets for supported AWS databases without requiring application downtime. "

Source: AWS Certified Solutions Architect – Official Study Guide, Security and Secrets Management section.

Amazon Macie: Detects sensitive data such as PII in S3 buckets using machine learning.

EventBridge Rule: Filters Macie findings for specific sensitive data events (e.g., SensitiveData).

SNS Notification: Provides real-time alerts to the security team for immediate action.

Amazon Macie Documentation,Amazon EventBridge Documentation

To decouple the monolith and enhance scalability, AWS best practice is to introduce an asynchronous message queue, such as Amazon SQS, between the web/API tier and the order-processing logic.

AWS Lambda functions consuming from the SQS queue provide serverless, auto-scaling processing without managing servers.

To track and reprocess failed orders, SQS supports dead-letter queues (DLQs). Messages that cannot be processed successfully after a configurable number of attempts are automatically moved to the DLQ, where operations teams or automated processes can inspect and reprocess them.

Why others are not correct:

B: ECS tasks can consume an SQS queue, but this requires managing container infrastructure and does not inherently provide as simple reprocessing/visibility as combining Lambda with a DLQ. Visibility timeout is not a tracking or archival mechanism.

C: Kinesis is a streaming service designed for ordered event streams, not primarily for order-queue semantics and DLQs; SQS is simpler and purpose-built for this pattern.

D: Long polling reduces empty responses and API calls but does nothing for tracking or reprocessing failed messages; without a DLQ, failed orders are harder to manage

Amazon Aurora PostgreSQL with Babelfish allows Aurora to understand SQL Server T-SQL and the SQL Server wire protocol. This enables applications to continue using SQL Server drivers, minimizing code changes (Option B).

Migration of schema and data is performed using AWS Schema Conversion Tool (SCT) and AWS Database Migration Service (DMS) (Option C), which is the AWS-recommended migration pattern for heterogeneous database migrations.

AWS DMS (Option E) does not rewrite application SQL.

RDS Proxy (Option D) does not translate SQL Server protocols.

Option A requires rewriting application queries, which contradicts the “minimal changes” requirement.

The current architecture tightly couples image upload, resizing, and storage into a single synchronous workflow handled by EC2 instances. This increases upload latency and degrades user experience. The optimal solution is to decouple upload from processing and offload work to managed services.

Option C allows users’ browsers to upload images directly to Amazon S3 using presigned URLs, bypassing the EC2 web servers entirely. This significantly reduces server load, network hops, and request latency while improving scalability and performance. Presigned URLs also maintain security by granting time-limited, scoped access to S3.

Option D complements this by using S3 Event Notifications to invoke an AWS Lambda function whenever a new image is uploaded. The Lambda function performs the image resizing asynchronously and stores the processed image back in S3. This event-driven, serverless pattern eliminates tight coupling between upload and processing and ensures automatic scaling with minimal operational overhead.

Option A is unsuitable because Glacier is for archival storage and not designed for active uploads or processing. Option B still routes uploads through EC2, maintaining the bottleneck. Option E introduces unnecessary delays and inefficiency by resizing images on a schedule instead of reacting to upload events.

Therefore, C and D together provide the most performant, scalable, and operationally efficient image upload and processing architecture using AWS-native, event-driven services.

IAM Roles for Service Accounts (IRSA): IRSA allows you to associate an IAM role with a Kubernetes service account. This enables pods to assume the IAM role and access AWS resources securely.

Annotating Service Accounts: By annotating Kubernetes service accounts with the ARN of the IAM role, you establish the association required for IRSA.

OIDC Identity Provider: EKS clusters use OpenID Connect (OIDC) to authenticate service accounts. Setting up a trust relationship between the IAM role and the OIDC provider allows the Kubernetes service account to assume the IAM role.

S3 Block Public Access only blocks public access—not explicitly allowed access.

An S3 bucket policy can explicitly permit access only from specific source IP addresses using the aws:SourceIp condition. This allows secure, direct HTTP access to the S3 object from known firewall IP addresses.

Static website hosting (Option A) requires the bucket to be public and is blocked by the enabled S3 Block Public Access setting.

CloudFront with OAC (Option C) is unnecessary and adds cost and complexity.

Lambda (Option D) introduces operational overhead and is not needed since S3 policies can enforce IP restrictions directly.

AWS recommends using AWS IAM Identity Center (formerly AWS SSO) for centralized authentication and access control across multiple accounts in an AWS Organization, especially when integrating with an external IdP.

From AWS Documentation:

“Use IAM Identity Center to provide centralized access to multiple AWS accounts or applications. You can integrate with an external IdP via SAML 2.0. Assign users permissions through permission sets that define the roles users can assume.”

(Source: AWS IAM Identity Center User Guide)

Why B and E are correct:

E enables centralized identity federation using IAM Identity Center with your external IdP.

B uses permission sets to apply least-privilege access roles to users and groups across accounts, in alignment with the principle of least privilege.

Why others are incorrect:

Option A: IAM users in each account break centralized access model and are hard to manage at scale.

Option C: Managing individual IAM roles and inline policies across accounts is not scalable.

Option D: Per-account SAML providers are redundant when using IAM Identity Center, which provides centralized federation.

To meet the requirements for tagging and preventing instance creation or deletion without proper tags, the company can use a combination of AWS Organizations tag policies and service control policies (SCPs).

Tag Policies: These enforce specific tag values across resources. Creating a tag policy with required values (e.g., sensitive, non-sensitive) and attaching it to the appropriate organizational unit (OU) ensures consistency in tagging.

SCPs: SCPs can be used to enforce compliance by preventing instance creation without a tag and preventing tag deletion. These policies control actions at the account level across the organization.

Key AWS features:

Tag Policieshelp standardize tags across accounts, andSCPsenforce governance by restricting actions that violate the policies.

AWS Documentation: AWS best practices recommend using tag policies and SCPs to enforce compliance across multiple accounts within AWS Organizations​.

Amazon Elastic File System (EFS) is a managed file storage service that can be mounted across multiple EC2 instances. It provides a scalable and high-performing solution to share data among instances within a VPC.

High Performance: EFS provides scalable performance for workloads that require high throughput and IOPS. It is particularly well-suited for applications that need to share data across multiple instances.

Ease of Use: EFS can be easily mounted on multiple instances across different Availability Zones, providing a shared file system accessible to all the instances within the VPC.

Security: EFS can be configured to ensure that data remains within the VPC, and it supports encryption at rest and in transit.

Why Not Other Options?:

Option A (Amazon S3 bucket with APIs): While S3 is excellent for object storage, it is not a file system and does not provide the low-latency access required for shared data between instances.

Option B (S3 bucket as a mounted volume): S3 is not designed to be mounted as a file system, and this approach would introduce unnecessary complexity and latency.

Option C (EBS volume shared across instances): EBS volumes cannot be attached to multiple instances simultaneously. It is not designed to be shared across instances like EFS.

AWS References:

Amazon EFS- Overview of Amazon EFS and its features.

Best Practices for Amazon EFS- Recommendations for using EFS with multiple instances.

Amazon S3 is the most cost-effective option for archiving data. Using AWS DMS to migrate to S3 in Apache Parquet format provides an optimized columnar format for analytics. By storing in S3 Glacier Flexible Retrieval, costs are minimized while maintaining compliance with retention. When queries are required, Athena can run SQL queries directly on archived data in S3 without provisioning infrastructure. Options A and B rely on maintaining RDS or EC2 instances, which increases cost and operational overhead. Option C requires full restores to EC2 before running queries, which is slow and inefficient. Therefore, D provides the lowest operational overhead and direct query capability with Athena.

AWS guidance for NAT Gateway recommends deploying “a NAT gateway in each Availability Zone and configure your routing to ensure that resources use the NAT gateway in the same Availability Zone.” This provides “zone-independent architecture” and avoids cross-AZ data processing charges and single-AZ failures. Option B creates a single point of failure and incurs cross-AZ egress charges when private subnets in other AZs traverse a centralized NAT. NAT instances (C) are legacy, require manual scaling/failover/patching, and are not recommended for production HA. Option D is not supported (NLB cannot front a NAT Gateway as a target). With steady, uniform load, per-AZ NAT Gateways deliver high availability with predictable cost; routing each private subnet to its local NAT Gateway maintains security (no inbound initiated connections) and resilience. This meets the requirement for cost-effective, secure outbound connectivity across multiple AZs while preserving availability.

Comprehensive and Detailed 250 to 300 words of Explanation (AWS documentation-based, no links):

Amazon Macie is the AWS managed service specifically built to discover, classify, and protect sensitive data in Amazon S3, including many common types of personally identifiable information (PII). Macie uses automated analysis and machine learning to identify sensitive data patterns at scale and produces findings that can be reviewed, prioritized, and integrated into security workflows. Because the environment spans hundreds of buckets, millions of objects, and multiple Regions, the key requirement is to minimize operational overhead while achieving broad coverage.

Option C is therefore the best fit: enabling Macie and configuring it to evaluate the targeted buckets provides a centralized, managed approach without building custom scanners, maintaining parsing logic, or operating distributed processing pipelines. Macie is designed for large-scale S3 estates and reduces ongoing maintenance compared with bespoke solutions.

Option A is incorrect because Amazon Detective is used to investigate and analyze security findings and relationships, not to classify S3 object content for PII. Option B is incorrect because Trusted Advisor provides best-practice checks (cost, security posture items, limits), but it does not inspect S3 object contents to detect PII. Option D would require building and operating custom Lambda scanning across millions of objects, handling pagination, retries, file types, performance tuning, and cost controls—high operational overhead and ongoing maintenance, which the company wants to avoid.

Therefore, C meets the requirement most directly and with the least operational burden by using the AWS service purpose-built for PII discovery in S3.

Creating a manual copy of the automated snapshot is the most operationally efficient option because it directly meets the requirement—retain a specific snapshot beyond the automated retention window—with the least added process and tooling. In Amazon RDS, automated backups and their snapshots are retained only for the configured backup retention period (up to the service maximum). When that retention period is exceeded, older automated snapshots are removed automatically. However, manual snapshots are retained until you explicitly delete them, so converting (copying) an automated snapshot to a manual snapshot is the standard operational approach to keep a point-in-time backup for long-term retention.

Option C is incorrect because you cannot extend automated snapshot retention beyond the maximum supported retention; also, setting “45 days” may still be within or beyond the service limits depending on the engine and configuration, and it doesn’t guarantee indefinite retention. Option B (export to S3) is not the most operationally efficient for the stated goal: exporting is a different workflow (often used for analytics, archiving in open formats, or cross-tool usage) and introduces extra steps, format considerations, and ongoing management in S3. Option D is also heavier operationally: native SQL Server backups require managing backup jobs, storage layout, restores, and permissions, and it shifts responsibility to the customer for operational correctness.

Therefore, A is the simplest and most AWS-native way to preserve an RDS snapshot long-term with minimal operational overhead.

To securely migrate an on-premises Oracle database to Amazon Aurora, the following steps are recommended:

Use AWS Schema Conversion Tool (AWS SCT) and AWS Database Migration Service (AWS DMS): AWS SCT helps convert the source database schema to a format compatible with the target database (Aurora). AWS DMS facilitates the actual data migration, ensuring minimal downtime and data integrity.

Use AWS Site-to-Site VPN: Establishing a Site-to-Site VPN connection provides a secure and encrypted tunnel between the on-premises environment and AWS. This ensures that data transferred during the migration is protected against interception and unauthorized access.

Amazon EFS integrates natively with Lambda, supports shared, persistent storage, and allows independent updates without redeploying functions. EBS and /tmp are not supported for this use case; FSx is unnecessary.

Amazon EFS is a “fully managed, elastic, shared file system” that provides NFSv4 access across many EC2 instances and AZs, ideal for lift-and-shift of Linux NFS workloads. AWS DataSync “automates moving data between on-premises NFS servers and Amazon EFS,” performing incremental, parallel transfers with encryption and integrity checks, enabling cutover with minimal disruption. FSx for Lustre (A) targets HPC and S3-backed workloads, not general NFS shares. FSx for Windows (D) exposes SMB, not NFS. S3 (C) is object storage and not directly NFS-accessible by multiple EC2 hosts. EFS + DataSync satisfies NFS compatibility, multi-client access, and low-touch migration while remaining cost-effective for a 200-GB dataset.

EC2 Image Builder is the AWS-managed service specifically designed to automate the creation, patching, hardening, and testing of AMIs.

For this scenario, best practice is to:

Use EC2 Image Builder pipelines to generate new golden AMIs whenever features are deployed or on a schedule.

Include build components such as update-linux to automatically apply OS security patches and updates during image creation.

Deploy the new AMIs through existing Auto Scaling groups, ensuring that every newly launched instance is already patched and compliant.

This approach:

Prevents OS vulnerabilities from being introduced at deployment time.

Scales across dozens of applications because AMI pipelines are reusable and automated.

Minimizes manual effort and reduces drift between instances.

Amazon Inspector (Option A) identifies vulnerabilities but does not itself patch AMIs.

AWS Backup (Option B) is for data protection, not vulnerability management.

Patch Manager (Option C) patches running instances, but the requirement is to ensure vulnerabilities are not introduced with new AMIs; golden image pipelines with EC2 Image Builder are the recommended solution.

AWS Budgets allows you to set custom cost and usage budgets. When actual or forecasted usage exceeds the threshold, AWS Budgets can automatically send alerts to an Amazon SNS topic. You can use AWS Cost Explorer in parallel for visual tracking of spending. This solution requires no code and has minimal operational overhead.

Comprehensive and Detailed Explanation (paraphrased from AWS Solutions Architect guidance):

Amazon Route 53 latency-based routing is specifically designed to route users to the endpoint that provides the lowest latency, based on actual latency measurements between AWS Regions and users’ networks. This directly supports the goal of optimizing load times (performance).

For non-AWS endpoints (like an on-premises data center), Route 53 lets you create a latency routing record and associate it with the AWS Region that is closest to that endpoint. In this case, the on-premises data center is near us-west-1, so you associate that latency record with the us-west-1 Region.

Users for whom the eu-central-1 Region has lower latency will be routed to the AWS website in eu-central-1.

Users for whom the us-west-1–associated endpoint has lower latency will be routed to the on-premises website.

This matches AWS best practices for high-performance, low-latency architectures when you have multiple endpoints in different geographic locations, including a mix of AWS and on-premises resources.

Why the other options are not correct:

Option A (Failover routing):

Failover routing is designed for high availability and disaster recovery (active–passive), not for optimizing latency.

All traffic goes to the primary endpoint (eu-central-1) unless it is unhealthy. That does not choose the lowest-latency endpoint for each user, so it does not meet the “optimize load times as much as possible” requirement.

Option B (IP-based routing):

IP-based / geo-based policies are about directing users based on location information (IP/geo), not measured latency.

You would have to manually decide which IP ranges get which endpoint, which does not guarantee that each user goes to the lowest-latency endpoint. It’s less precise and less dynamic than latency-based routing.

Option D (Weighted routing):

Weighted routing splits traffic according to fixed weights (e.g., 50/50), regardless of user location or latency.

This is useful for testing, blue/green deployment, or gradual traffic shifting, but not for performance optimization per user.

Therefore, the only option that directly aligns with AWS guidance for performance optimization and latency-based traffic steering across Regions and on-premises endpoints is:

✅ C. Create a DNS record with a latency-based routing policy… associate the on-premises endpoint with us-west-1.

D is the best match because AWS Lambda is a serverless compute service that runs code without provisioning or managing servers, which directly aligns with the requirement for minimal infrastructure and minimal operational support. Lambda supports Python runtimes, so the team can deploy the selected component as a microservice using the same language. For workloads with hundreds of requests per second, Lambda is designed to handle request-driven execution and automatically scales by running more concurrent instances of the function in response to incoming traffic, without the customer needing to pre-size or manage capacity.

The other options introduce significantly more operational responsibility. Option A still requires managing EC2 instances (AMI lifecycle, patching, capacity planning, scaling policies) and Spot interruptions can add complexity for a web microservice test. Option B (Elastic Beanstalk) reduces some management overhead but still relies on underlying instances and environment management (platform updates, scaling configuration, health monitoring), and it is not a serverless model. Option C (EKS with self-managed nodes) has the highest operational burden here: cluster and node management, scaling node groups, patching, and Kubernetes operations—this conflicts with “minimal operational support.”

Lambda fits the serverless microservices goal: deploy code as small independent services, integrate with other managed services (commonly API-driven invocation patterns), and rely on AWS-managed scaling and availability mechanisms rather than administering fleets or clusters.

Amazon RDS Proxy: RDS Proxy is a fully managed, highly available database proxy that makes applications more resilient to database failures by pooling and sharing connections, and it can automatically handle database failovers.

Reduced Failover Time: By using RDS Proxy, the connection management between the application and the database is improved, reducing failover times significantly. RDS Proxy maintains connections in a connection pool and reduces the time required to re-establish connections during a failover.

Configuration:

Create an RDS Proxy instance.

Configure the proxy to connect to the RDS for PostgreSQL DB instance.

Modify the application configuration to use the RDS Proxy endpoint instead of the direct database endpoint.

Operational Benefits: This solution provides high availability and reduces application timeouts during failovers with minimal changes to the application code.

To manage EC2 instances with AWS Systems Manager (SSM), the EC2 instance must be configured as a managed instance by attaching an IAM role that has the AmazonSSMManagedInstanceCore managed policy.

This policy allows:

SSM agent to register the instance with SSM

Perform actions like patching, automation, session management, inventory collection, etc.

Access to SSM endpoints (via internet or VPC endpoint if needed)

Since the EC2 instance already has an IAM role, the least operational overhead option is to attach the required policy to the existing role (Option C). No need to create new IAM roles or users, which simplifies management and adheres to the principle of least privilege.

Patching can then be automated via SSM Patch Manager, ensuring consistency, compliance, and operational efficiency.

???? References:

SSM Managed Instance Setup

AmazonSSMManagedInstanceCore Policy

Patching EC2 with SSM

AWS documentation states that the correct and least-privilege method for cross-account SNS-to-SQS integration is:

• Add specific SNS topic ARNs to the SQS queue policy.

• Allow only those topics to publish messages to the queue.

• Ensure SNS has permission to publish to the specific queue ARN.

This ensures strict scoping and adheres to least privilege.

Options A and D grant overly broad permissions. Option B allows publishing to any queue, which violates least privilege.

AWS recommends using Amazon ElastiCache as an in-memory caching layer in front of relational databases such as Amazon RDS to offload read traffic and significantly improve performance for read-heavy workloads. ElastiCache (Redis OSS) provides microsecond latency and can cache the results of frequent or expensive queries without requiring any change to the underlying database schema or engine. This directly addresses the requirement to improve performance without changing the database structure and with minimal operational overhead, because ElastiCache is a fully managed service (patching, failure detection, replacement, etc., are handled by AWS).

Option A (DynamoDB) would require a full data migration and application changes, including schema and query rewrites.

Option C (Memcached on EC2) introduces additional management overhead for EC2 instances (scaling, patching, HA).

Option D (DAX) is a caching layer only for DynamoDB and cannot be used directly with Amazon RDS.

The AWS Cost and Usage Report (CUR) delivers detailed, line-item billing data to Amazon S3. AWS recommends querying CUR with Amazon Athena by creating external tables over the CUR S3 location (partitioned by time) to produce daily cost aggregations such as NAT Gateway (EC2:NatGateway) usage and cost. Amazon QuickSight natively connects to Athena as a data source to build and share dashboards with visuals (tables, time series) filtered from the start of the current month. DataSync (A, C) is a file transfer service and cannot query data. CloudWatch dashboards (C, D) visualize metrics/logs, not CUR datasets. Therefore, using Athena to query CUR and QuickSight to present a daily NAT gateway cost dashboard is the most direct and operationally efficient approach.

Given the large size (180 TB) of the database and the time constraint,AWS Snowball Edge Storage Optimizedis the best solution. Snowball Edge allows for the physical transfer of large datasets to AWS efficiently without relying on slow internet connections.AWS DMSandSCTcan be used to perform ongoing replication of any changes made during the migration, ensuring minimal downtime.

Option B (VPN): Using a 100 Mbps internet connection would take far too long to transfer 180 TB.

Option C (Direct Connect): Establishing a 10 Gbps Direct Connect link might not be feasible within the 2-week timeframe.

Option D (DataSync over internet): With the existing internet connection, DataSync would also take too long.

AWS References:

AWS Snowball Edge

AWS DMS

Amazon Aurora MySQL–compatible offers a distributed, fault-tolerant storage system with Multi-AZ high availability and supports Aurora Replicas that “share the same underlying storage” for low-latency reads. Aurora provides Aurora Auto Scaling to “automatically add or remove Aurora Replicas based on load,” ideal for unpredictable, read-heavy workloads. This architecture offloads reads from the writer and maintains HA through automatic failover. Amazon RDS Single-AZ (B) lacks HA. Redshift (A) is a data warehouse, not a transactional DB. ElastiCache (D) can reduce read pressure but does not provide durable read replicas or automatic HA scaling at the database tier. Aurora’s design directly addresses the requirement to automatically scale reads while maintaining availability, matching Well-Architected guidance to use managed, elastic services for variable demand.

Amazon S3 Intelligent-Tiering automatically moves objects between frequent and infrequent access tiers based on access patterns, which minimizes cost without performance impact. Storing photo metadata in Amazon DynamoDB provides fast and scalable lookup by user or tag.

From AWS Documentation:

“The S3 Intelligent-Tiering storage class automatically optimizes storage costs by moving data between frequent and infrequent access tiers when access patterns change.”

(Source: Amazon S3 User Guide – Intelligent-Tiering)

Why B is correct:

S3 Intelligent-Tiering optimizes storage automatically for cost without lifecycle management overhead.

DynamoDB stores small metadata items and S3 URLs, enabling efficient queries and photo lookups.

This architecture scales globally, integrates seamlessly with applications, and minimizes operational cost.

Why other options are incorrect:

A & D: DynamoDB is not intended for storing binary objects like images.

C: Lifecycle rules are static and don’t adapt dynamically to unpredictable access patterns.

AWS provides EBS Block Public Access at the AWS Organizations level, which, when enabled, prevents EBS snapshots from being shared publicly across all member accounts. This is an organization-wide control that requires minimal configuration and no ongoing monitoring to prevent public sharing.

This directly satisfies the requirement:

Covers all accounts managed by Organizations.

Explicitly prevents public snapshot sharing.

Has the least operational overhead because it is a single centralized control.

AWS Config (Option A) only monitors and alerts, but does not prevent public sharing.

An IAM policy in the root account (Option C) is harder to enforce consistently across all accounts and may not cover all permission paths.

CloudTrail (Option D) only logs events and does not prevent public access.

The most scalable and managed solution for streaming ingestion, real-time transformation, and delivery to Amazon S3 is Amazon Kinesis Data Streams, Amazon Managed Service for Apache Flink, and Amazon Kinesis Data Firehose.

From AWS Documentation:

“Amazon Kinesis Data Streams enables real-time processing of streaming data at massive scale. With Apache Flink on Kinesis Data Analytics, you can process data streams in near real-time, then use Amazon Kinesis Data Firehose to reliably deliver that data to S3.”

(Source: Amazon Kinesis Developer Guide)

Why A is correct:

Fully managed: All services involved are serverless and managed.

Real-time ingestion: Kinesis Data Streams supports sub-second latency and can handle high-throughput workloads like 100,000+ devices.

Near real-time processing: Apache Flink is designed for continuous stream processing with complex event handling.

Efficient delivery: Kinesis Firehose delivers processed data directly to S3 with retry and backup capability.

Why other options are incorrect:

Option B: SQS is not optimized for real-time streaming at high volume.

Option C: EC2 + Kafka + EMR adds high operational overhead and cost.

Option D: EventBridge is event-driven, not designed for high-throughput streaming; AWS Batch is unsuitable for near real-time processing.

AWS Backup Audit Manager automates auditing and reporting of backup activity and compliance, while AWS Config provides visibility into configuration changes. Together, they provide the simplest, most automated, and compliant backup monitoring solution.

From AWS Documentation:

“AWS Backup Audit Manager automatically audits backup activity across AWS resources. You can use predefined or custom frameworks to monitor backup compliance and encryption status.”

(Source: AWS Backup Audit Manager User Guide)

Why D is correct:

Ensures centralized visibility into all backup jobs.

Verifies encryption status automatically.

Generates ready-to-use reports with minimal operational overhead.

Complies with regulatory requirements for data protection.

Why others are incorrect:

A & C: Custom Lambda automation increases maintenance effort.

B: Manual checking is operationally inefficient and error-prone.

Explanation (AWS Docs):

A: Use Direct Connect with a private VIF to ensure private connectivity from on-premises to AWS.

E: Peer the networking VPC with VPCs in the accounts hosting S3 buckets to allow private routing.

“A private virtual interface enables private connectivity to your VPC.”

— Direct Connect Private VIF

AWS Key Management Service (AWS KMS) supports multi-Region keys, which are managed customer master keys that can be replicated to multiple Regions and treated as a single logical key. This allows applications in different Regions to encrypt and decrypt data using equivalent keys while AWS handles key replication, durability, and availability.

KMS is a fully managed key service, so the company does not need to operate hardware or manage low-level key storage. Tags combined with attribute-based access control (ABAC) let you enforce fine-grained access to keys by using IAM policies and condition keys, giving centralized and flexible access control with low operational overhead.

CloudHSM (Options C and D) requires managing HSM clusters, scaling, backups, and manual key operations, which significantly increases operational burden. Importing key material in KMS (Option B) adds lifecycle complexity and is unnecessary when native multi-Region keys exist.

The key issue is repeated reads of identical data from an RDS MySQL database, which leads to unnecessary database load and degraded performance. The AWS-recommended solution for this pattern is to introduce an in-memory cache between the application and the database.

Amazon ElastiCache (Redis OSS) is purpose-built for caching frequently accessed data with microsecond-level latency. By caching identical query results, the backend EC2 instances can serve responses directly from memory instead of repeatedly querying the database. This significantly reduces read pressure on the RDS instance and improves overall application performance and scalability.

Option B is correct because Redis integrates cleanly with EC2-based applications and supports advanced caching patterns such as key expiration, eviction policies, and fine-grained control over cached objects. This approach also improves resilience during traffic spikes by offloading work from the database.

Option A is incorrect because SNS is a messaging service and does not cache or accelerate database queries. Option C is incorrect because DynamoDB Accelerator (DAX) works only with DynamoDB tables, not Amazon RDS. Option D is designed for streaming data ingestion and analytics, not for optimizing synchronous database access.

Therefore, B is the correct solution because it addresses the root cause of the performance problem by caching repeated database reads, following AWS best practices for high-performing application architectures.

Key Requirements:

Handle traffic spikes efficiently and reduce latency caused by cold starts.

Optimize costs during low traffic periods.

Analysis of Options:

Option A:

Provisioned Concurrency:Reduces cold start latency by pre-warming Lambda environments for the required number of concurrent executions.

AWS Application Auto Scaling:Automatically adjusts provisioned concurrency based on demand, ensuring cost optimization by scaling down during low traffic.

Correct Approach:Provides a balance between performance during traffic spikes and cost optimization during idle periods.

Option B:

Using EC2 instances with Auto Scaling introduces unnecessary complexity for a serverless architecture. It requires additional management and does not address the issue of cold starts for Lambda.

Incorrect Approach:Contradicts the serverless design philosophy and increases operational overhead.

Option C:

Setting a fixed concurrency level ensures performance during spikes but does not optimize costs during low traffic. This approach would maintain provisioned instances unnecessarily.

Incorrect Approach:Lacks cost optimization.

Option D:

Using EventBridge Scheduler for periodic invocations may reduce cold starts but does not dynamically scale based on traffic demand. It also leads to unnecessary invocations during idle times.

Incorrect Approach:Suboptimal for high traffic fluctuations and cost control.

AWS Solution Architect References:

AWS Lambda Provisioned Concurrency

AWS Application Auto Scaling with Lambda

S3 Object Lock: Enables Write Once Read Many (WORM) protection for data, preventing objects from being deleted or modified for a set retention period.

S3 Versioning: Helps maintain object versions and ensures a recovery path for accidental overwrites.

CloudFront Distribution: Ensures secure and efficient public access by serving content through an edge-optimized delivery network while protecting S3 data with OAC.

Bucket Policy for OAC: Restricts public access to only the CloudFront origin, ensuring maximum security.

Amazon S3 Object Lock Documentation

Amazon Managed Service for Apache Flink (formerly Kinesis Data Analytics) is a fully managed, serverless service for real-time processing of streaming data. You can consume data from Kinesis Data Streams, perform anomaly detection, and then output the results to another Kinesis stream. This approach is loosely coupled, fully managed, and does not require modifying the application code.

AWS Documentation Extract:

“Amazon Managed Service for Apache Flink enables you to process streaming data in real time, integrating with Kinesis Data Streams as source and sink, and is fully managed and serverless.”

(Source: Apache Flink on AWS documentation)

B: S3/Redshift is not real-time and adds complexity.

C, D: EC2/ECS solutions are not serverless or fully managed.

This solution is the most scalable and efficient way to handle large volumes of survey data while automating sentiment analysis:

API Gateway and SQS: The survey results are sent to API Gateway, which forwards the data to an SQS queue. SQS can handle large volumes of messages and ensures that messages are not lost.

AWS Lambda: Lambda is triggered by polling the SQS queue, where it processes the survey data.

Amazon Comprehend: Comprehend is used for sentiment analysis, providing insights into customer satisfaction.

DynamoDB with TTL: Results are stored in DynamoDB with aTime to Live (TTL)attribute set to expire after 365 days, automatically removing old data and reducing storage costs.

Option B (EC2 API): Running an API on EC2 requires more maintenance and scalability management compared to API Gateway.

Option C (S3 and Rekognition): Amazon Rekognition is for image and video analysis, not sentiment analysis.

Option D (Amazon Lex): Amazon Lex is used for building conversational interfaces, not sentiment analysis.

AWS References:

Amazon Comprehend for Sentiment Analysis

Amazon SQS

DynamoDB TTL

Amazon S3 supports one Lifecycle configuration per bucket that can contain multiple rules. Lifecycle rules can include filters, including object size filters: ObjectSizeGreaterThan and ObjectSizeLessThan. You can combine rules so that one targets large objects and another provides a default expiration. Here, configure Rule 1 with a size filter ObjectSizeGreaterThan = 1 TB and Expiration = 2 days (48 hours) to purge very large videos early. Configure Rule 2 with Expiration = 7 days without a size filter to serve as a catch-all maximum retention for all objects. Options B and D are invalid because S3 does not allow multiple Lifecycle configurations per bucket. Option C cannot distinguish large files; it would delete all files at the same schedule without honoring the 48-hour policy for > 1 TB objects. This single configuration with two rules meets both retention targets with minimal overhead and full S3-native capability.

AWS Key Management Service (AWS KMS) provides multi-Region keys, allowing the same key to be used across Regions without managing your own hardware or key replication.

Multi-Region keys are fully managed and support attribute-based access control (ABAC) using tags and condition keys.

CloudHSM (Options C and D) requires full key lifecycle management, synchronization, and hardware operations, resulting in significantly higher overhead.

Imported key material (Option B) increases key-management responsibility.

=====================================================

The optimal solution for low-latency global caching with disaster recovery and cross-Region replication is to use Amazon ElastiCache Global Datastore for Redis OSS.

A Global Datastore enables fully managed cross-Region replication and supports automatic failover by promoting read replica clusters in another Region.

ElastiCache ensures encryption in-transit and at-rest, meeting compliance and security requirements.

It ' s a fully managed AWS-native feature, reducing operational effort compared to setting up DMS-based or snapshot-based replication manually.

Other options (A, C, D):

Require manual setup and management (e.g., custom DMS pipelines, snapshots).

Do not offer real-time replication or failover without manual intervention.

???? Reference:

ElastiCache Global Datastore for Redis

For short, frequent daily spikes (15–20 minutes), the most cost-effective scaling behavior typically comes from target tracking scaling driven by a request-based metric, because it continuously adjusts capacity to maintain a chosen target value rather than adding/removing capacity in coarse steps. Option D uses a custom CloudWatch metric representing the number of GET requests (sourced from ALB access logs, application instrumentation, or request counting logic) and applies target tracking so the Auto Scaling group scales out and in proportionally to demand. This helps avoid both under-provisioning (bad performance during spikes) and over-provisioning (wasted cost between spikes), which is exactly what cost-optimized elasticity aims for.

Option A (simple scaling) is usually less cost-efficient for bursty traffic because it relies on fixed adjustments and alarm thresholds, often with cooldowns. With 15–20 minute spikes, simple scaling can react too slowly, overshoot, or oscillate, leaving extra instances running after the spike or failing to ramp fast enough at the start. Option B (predictive scaling) is intended for regular, forecastable demand patterns, but it’s not always the most cost-effective choice for repeated short spikes because it can pre-scale conservatively and may require more historical data and tuning; it is often paired with dynamic scaling anyway. Option C uses UnhealthyHostCount, which is a health signal—not a demand signal—and scaling on unhealthy hosts can increase cost without addressing request volume; it may also mask underlying application issues rather than scaling to meet load.

Because the requirement explicitly allows standard or custom metrics and focuses on scaling based on request volume, D best matches a cost-efficient, responsive approach that directly tracks incoming workload.

The company wants to move from an on-premises Docker environment to fully managed AWS services with persistent storage. The best fit is:

Amazon ECS with AWS Fargate launch type: This is a serverless container orchestration solution where AWS manages the underlying infrastructure, removing the need to manage EC2 or Kubernetes nodes.

Amazon EFS (Elastic File System): This is a fully managed, scalable, and shared file system for use with ECS tasks. It supports persistent storage for containers, replacing the local volumes used on-premises.

This combination (ECS + Fargate + EFS) is fully managed and requires no manual server maintenance.

Option A uses EKS with self-managed nodes, which is not fully managed.

Option C (DynamoDB) is for structured key-value storage, not for persistent file storage.

Option D uses ECS with EC2 launch type, which is not serverless and requires managing instances.

???? Reference:

Using Amazon ECS with AWS Fargate

Mounting EFS volumes in ECS tasks

To build a distributed, serverless, event-driven workflow with minimal operational overhead, AWS Step Functions orchestrating AWS Lambda is the best fit. Step Functions provides a managed state machine service that coordinates multiple steps, handles retries and error handling patterns, and maintains execution state without requiring the company to run orchestration servers. Lambda provides serverless compute for each workflow task, scaling automatically with demand and eliminating server management.

Option D directly matches “performing different aspects of the workflow” because Step Functions is specifically designed to model multi-step business processes and coordinate activities across services. It also aligns with “minimize operational overhead” because both Step Functions and Lambda are managed services: there are no instances to patch, no cluster management, and scaling is handled by the platform.

Option B contradicts the serverless goal by deploying the application on EC2 instances, increasing operational overhead (capacity management, patching, scaling, and availability concerns). Option C mixes eventing with scheduling: EventBridge can route events, but invoking Lambda “on a schedule” is not the same as orchestrating a multi-step workflow with branching, waiting, retries, compensation, and state tracking. EventBridge is excellent as an event bus, but Step Functions is the purpose-built workflow orchestrator. Option A is a mismatch because AWS Glue is primarily an ETL/data integration service; while it can run jobs and triggers, it is not the general workflow orchestrator for arbitrary application steps, and using it to invoke Lambda for a broad workflow is less direct and typically less operationally clean than a Step Functions state machine.

Therefore, D is the most aligned solution for serverless, distributed workflow orchestration with low operational burden.

The correct answer is D because the application peak hours occur at the same time each day , and the performance issue happens specifically at the start of peak hours . This indicates that the company knows in advance when additional capacity will be needed. A scheduled scaling policy is the most appropriate solution because it allows the Auto Scaling group to increase capacity before demand rises, ensuring that instances are already launched and available when users begin accessing the application.

Dynamic scaling based on CPU or memory reacts only after utilization increases. Because launching EC2 instances takes time, reactive scaling can lead to delays and degraded user experience at the beginning of peak periods. That directly matches the problem described in the question, where performance is slow at the start but normalizes later once capacity catches up. Scheduled scaling solves this by making capacity available proactively.

Option A is incorrect because an Application Load Balancer distributes traffic among existing instances but does not add capacity ahead of predictable demand. Option B is incorrect because Auto Scaling does not natively scale on memory utilization unless a custom metric is configured, and even then it would still be reactive rather than proactive. Option C is also incorrect because CPU-based dynamic scaling reacts after demand increases and therefore may not prevent the startup lag during peak traffic.

AWS design guidance recommends using scheduled scaling when traffic patterns are predictable and occur at known times. This approach improves application responsiveness and user experience by making sure enough EC2 capacity is available before the workload surge begins.

Explanation (AWS Docs):

DynamoDB Accelerator (DAX) is a fully managed, in-memory cache specifically for DynamoDB. It reduces read load and latency without requiring code changes (only SDK config). This is the least development effort.

“Amazon DynamoDB Accelerator (DAX) is a fully managed, highly available in-memory cache for DynamoDB that delivers microsecond read performance and requires minimal application changes.”

— Amazon DAX

In Amazon Aurora, a custom endpoint is a feature that allows you to create a load-balanced endpoint that directs traffic to a specific set of instances in your Aurora DB cluster. This is particularly useful when you want to route traffic to a subset of instances that have different configurations or when you want to isolate specific workloads (e.g., reporting queries) to certain instances.

Custom Endpoint: The correct solution is to create a custom endpoint that includes the three Aurora Replicas that the department wants to use for near-real-time reporting. This custom endpoint will distribute the reporting queries only across the three selected replicas with the specified compute and memory configurations, ensuring that these queries do not affect the rest of the DB cluster.

Other Options:

Option B (Create a three-node cluster clone): This would create a separate cluster with its own resources, but it is not necessary and could incur additional costs. Also, it doesn ' t leverage the existing replicas.

Option C (Use any of the instance endpoints): This would involve manually managing connections to individual instances, which is not scalable or automatic.

Option D (Use the reader endpoint): The reader endpoint would distribute the read queries across all replicas in the cluster, not just the selected three. This would not meet the requirement to limit the reporting queries to only three specific replicas.

AWS References:

Amazon Aurora Endpoints- Provides detailed information on the different types of endpoints available in Aurora, including custom endpoints.

Custom Endpoints in Amazon Aurora- Specific documentation on how to create and use custom endpoints to direct traffic to selected instances in an Aurora cluster.

This workload has two distinct needs: (1) a managed way for external clients to upload files using a familiar managed file transfer protocol, and (2) an automated, low-ops method to run a watermarking step and store the resulting images as objects. AWS Transfer Family provides a fully managed capability to receive files over protocols such as SFTP, while landing those files directly into Amazon S3 as objects. That directly satisfies the requirement that “uploaded images must be stored as objects” with minimal infrastructure management.

To automate watermarking with minimal operational overhead, invoking AWS Lambda is a strong fit. Lambda is serverless and scales automatically with incoming uploads, and it can run the watermarking logic as code without managing servers. Transfer Family supports managed workflows that can orchestrate post-upload actions; using a Transfer Family workflow to invoke a Lambda function provides a clean, managed, event-driven pipeline: clients upload via SFTP → objects land in S3 → workflow triggers watermark processing → output is written back to S3.

Option A adds operational burden by requiring an EC2-based application tier for watermarking, which increases patching and scaling responsibilities. Option B can work (S3 + Batch), but it requires building and operating the upload mechanism (REST APIs) and typically more orchestration than necessary for simple “upload then process” flows; Batch is better when you need large-scale, long-running batch compute rather than lightweight per-object processing. Option D is unsuitable because S3 Glacier Deep Archive is intended for long-term archival with slow retrieval, which conflicts with active workflow processing and watermark automation.

Therefore, C best meets the requirements using managed services end-to-end: Transfer Family for ingestion, S3 for object storage, and Lambda for automated watermarking.

This solution directly addresses the scalability and high availability requirements with minimal downtime.

Additional Reader Instances: Adding more reader instances to the Aurora cluster will distribute the read load, improving the performance of the application under heavy read traffic. Aurora reader instances automatically replicate the data from the writer instance, enabling you to scale out read operations.

Amazon RDS Proxy: RDS Proxy improves database availability by managing database connections more efficiently and providing a connection pool. This reduces the overhead on the Aurora cluster during peak loads, further enhancing performance and availability without requiring changes to the application code.

Why Not Other Options?:

Option A (EventBridge and Lambda): This doesn’t directly address the performance and availability issues. Logging state changes and adding reader nodes on failure events doesn’t provide proactive scalability.

Option B (Zero-Downtime Restart and Activity Streams): Zero-Downtime Restart (ZDR) is useful for minimizing downtime during maintenance but doesn’t directly improve scalability. Database Activity Streams are more for security monitoring than for performance enhancement.

Option D (ElastiCache for Redis): While adding a caching layer can help with read performance, it introduces complexity and may not be necessary if additional reader instances can handle the load.

AWS References:

Amazon Aurora Scaling- Information on scaling Aurora clusters with reader instances.

Amazon RDS Proxy- Details on how RDS Proxy can improve database performance and availability.

Comprehensive and Detailed 250 to 300 words of Explanation (AWS documentation-based, no links):

The company wants to increase security and reduce operational overhead for SQL Server relational databases. The most direct AWS managed service for SQL Server is Amazon RDS for SQL Server, which offloads routine database administration tasks such as provisioning, backups, patching (within managed service controls), monitoring integrations, and high availability management compared with running SQL Server on self-managed EC2.

Choosing Multi-AZ enhances availability and resilience by maintaining a synchronous standby in a separate Availability Zone with automated failover. This improves overall security posture and operational reliability because the platform handles common infrastructure failure scenarios without requiring custom clustering or manual intervention.

For encryption of sensitive data, integrating RDS with AWS KMS allows encryption at rest using KMS keys. Using an AWS managed key is operationally simple while still providing strong encryption controls, auditability, and centralized key management.

Option A still requires managing the database on EC2 (OS patching, SQL Server patching, backups, HA design, monitoring, recovery), which increases operational overhead significantly. Option C is not a database migration approach; S3 plus Macie does not provide a relational SQL Server engine for applications. Option D changes the data model by moving to DynamoDB and does not preserve SQL Server relational capabilities; CloudWatch Logs is not a data security control for database content.

Therefore, B best satisfies the goals: a managed relational database service (RDS for SQL Server) with built-in high availability (Multi-AZ) and encryption at rest via KMS, reducing ongoing operational work while improving security controls.

The Kubernetes Horizontal Pod Autoscaler (HPA) scales pods, not nodes. When the cluster runs out of node capacity, the HPA cannot schedule more pods.

On Amazon EKS, AWS recommends using the Kubernetes Cluster Autoscaler to automatically adjust the number of nodes in the cluster’s underlying Auto Scaling group based on pending pods.

Cluster Autoscaler watches for pods that cannot be scheduled because of insufficient resources and then scales out nodes automatically with minimal administrative overhead. It also scales in nodes when they are underutilized.

Why others are not correct:

A: Just tracking memory usage does not automatically scale nodes or integrate with Kubernetes scheduling.

C: A custom Lambda-based resizer is unnecessary undifferentiated heavy lifting compared to the native Cluster Autoscaler.

D: An Auto Scaling group is already typically used under EKS, but by itself it does not respond to pod scheduling pressure without Cluster Autoscaler.

For processing thousands of images and generating large files while minimizing manual tasks and operational overhead, usingAWS Batchis the best solution. AWS Batch allows you to run large-scale, parallel, and managed batch computing jobs without needing to manage the underlying infrastructure.

AWS Batch: Automates the image processing jobs, dynamically allocating the necessary resources based on the job requirements, which reduces operational overhead.

AWS Step Functions: Orchestrates the entire image processing workflow, ensuring that tasks are executed in the correct sequence, improving manageability.

Amazon S3: Stores the processed files, providing scalable and cost-effective storage.

Option A (ECS with EC2 Spot Instances): While cost-effective, managing ECS and Spot Instances involves more operational effort.

Option C (Lambda with EC2 Spot): Lambda functions have size and duration limitations, making them less suited for large image processing tasks.

Option D (EC2 with Step Functions): Managing EC2 instances involves more overhead than using AWS Batch.

AWS References:

AWS Batch

AWS Step Functions

To send data privately from on-premises to S3 buckets across multiple AWS accounts:

A: Using AWS Direct Connect with a private virtual interface (VIF) enables private connectivity from on-premises into the VPC.

E: After establishing the central VPC in a networking account, use VPC peering to allow access to S3 buckets across multiple AWS accounts.

“Direct Connect private virtual interface (VIF) allows private IP connectivity to VPC resources.”

“VPC Peering enables routing of traffic between VPCs using private IP addresses.”

— AWS Direct Connect Documentation

— VPC Peering Guide

Why not others:

B: Public VIF sends traffic over public AWS services—not fully private.

C: Interface endpoint is useful within a single account/VPC context.

D: Gateway endpoint doesn ' t solve inter-account routing.

For globally distributed consumers of REST APIs, API Gateway edge-optimized endpoints “route requests to the nearest CloudFront edge location, which then forwards them to your API in the [home] Region,” reducing latency for users worldwide and scaling automatically for unknown or spiky traffic. By contrast, Regional endpoints are intended for clients within the same or nearby Region and do not provide global edge acceleration. AWS Lambda provides automatic scaling for serverless backends; latency can be further reduced by right-sizing memory (which also increases CPU) and, when needed, enabling provisioned concurrency to keep functions initialized and eliminate cold starts for critical paths. Using NLBs across Regions is not serverless and adds operational comp lexity without CloudFront edge acceleration. Therefore, combining API Gateway edge-optimized REST APIs with Lambda meets the requirements of minimal global latency and unknown initial traffic.

Amazon RDS for MySQL supports the creation of read replicas, which are read-only copies of the primary database instance. By offloading read-heavy operations, such as reporting queries, to a read replica:

Performance Improvement: The primary DB instance is relieved from the additional load, reducing the likelihood of timeouts during order processing.

Data Consistency: Read replicas use asynchronous replication, ensuring that they have up-to-date data for accurate reporting.

Scalability: Multiple read replicas can be created to handle increased read traffic.

This approach allows employees to continue running necessary reports without impacting the performance of the ordering application.

Workload Discovery on AWS automatically scans AWS accounts and Regions, identifies deployed resources, and generates relationship-aware architecture diagrams. This provides a complete workload map with minimal operational effort.

Systems Manager Inventory (Option A) collects instance metadata but does not build multi-account architecture diagrams.

Step Functions (Option B) and X-Ray (Option D) require manual diagramming and do not meet the operational-efficiency requirement.

=====================================================

Lambda@Edge allows you to run functions at CloudFront edge locations, enabling modifications to content before it ' s delivered to users, including compression — which directly reduces data transfer cost.

“Lambda@Edge can be used to compress or modify your content before delivering it to end users, which reduces the amount of data transferred.”

— Lambda@Edge Use Cases

Since code modifications aren’t allowed, using Lambda@Edge is a non-invasive way to:

Compress responses.

Reduce transfer size = lower CloudFront cost.

Incorrect Options:

B: S3 Transfer Acceleration improves speed, not cost.

C: Caching doesn’t help if content is always unique (single-use).

D: Multipart uploads help with large file uploads, not transfers.

Amazon RDS Blue/Green Deployments provide a safe and straightforward way to make database changes with minimal downtime and risk. Blue/Green deployments create an exact copy ( " green " ) of your production environment ( " blue " ) where you can make schema changes and run tests. After validation, you can promote the green environment to production with a single click or API call, achieving near-zero downtime and maximum availability. This is the AWS-recommended method for deploying major database changes in a way that minimizes impact to users and maximizes uptime.

Reference Extract from AWS Documentation / Study Guide:

" Amazon RDS Blue/Green Deployments enable you to make changes to your database environment safely. You can perform schema updates and feature testing in a fully managed staging environment and switch over with minimal downtime, ensuring the highest availability. "

Source: AWS Certified Solutions Architect – Official Study Guide, Database and Migration section; Amazon RDS Blue/Green Deployments Documentation.

AWS Batch supports Windows-based jobs and automates provisioning and scaling of compute environments. Paired with AWS Fargate, it removes the need to manage infrastructure. This solution requires the least operational overhead and is cloud-native, providing flexibility and scalability.

The problem is scale-out latency: when traffic spikes, new instances take time to launch, initialize, and pass health checks. This can temporarily degrade user experience even if Auto Scaling eventually adds enough capacity. The goal is to improve responsiveness to sudden spikes cost-effectively.

A key Auto Scaling feature for this situation is an EC2 Auto Scaling warm pool. A warm pool maintains a set of pre-initialized instances (stopped or running, depending on configuration) that Auto Scaling can rapidly transition into service. This reduces the time needed for instance launch and initialization, enabling faster scale-out during unexpected surges.

Option B is best because it supports the application’s peak needs by ensuring Auto Scaling can scale up to the required maximum capacity and keeps warm capacity ready. While a warm pool has some cost (especially if instances are kept running), it is typically more cost-effective than permanently running peak capacity all the time. It improves user experience by minimizing cold start delays during spikes.

Option A is counterproductive: lowering minimum capacity increases the likelihood of scale-out delays. Option C is illogical as written (“increase maximum to meet normal demand”); maximum should reflect peak potential, and a warm pool without sufficient max headroom will not solve the spike problem. Option D uses scheduled scaling, but the spike is unexpected, and increasing instance sizes increases cost and does not address launch delay; it may also reduce horizontal scaling flexibility.

Therefore, B is the most cost-effective way to improve scale-out performance and protect user experience during unexpected bursts.

An Application Load Balancer supports path- and host-based routing, which makes it ideal for routing requests based on subdomains. EC2 Auto Scaling ensures that the number of instances adjusts dynamically based on traffic, which helps manage cost and performance during predictable peak hours.

AWS Lake Formation provides fine-grained (column-level) access control for data stored in S3. Using a Lake Formation blueprint ensures database ingestion is automated and governed.

QuickSight can query Athena, and Athena honors Lake Formation permissions, enforcing column-level controls automatically.

Options A, B, and C rely on manual filtering or IAM policies, which cannot enforce column-level authorization for SQL queries.

The key requirements are (1) MySQL compatibility for existing applications and (2) the ability to scale automatically during demand spikes for a transactional workload. Amazon Aurora (MySQL-compatible) satisfies compatibility while providing managed database capabilities designed for high performance and scalability. Using AWS Database Migration Service (DMS) is operationally efficient for migration because it supports moving data from on-premises databases to AWS with options for continuous replication to minimize downtime, which is commonly required for transactional systems.

Turning on Aurora Auto Scaling (for Aurora Replicas) allows the database tier to automatically add or remove read replicas based on demand, which helps absorb increased read traffic without manual intervention. This meets the “scale automatically” requirement more directly than simply scaling storage. (Write scaling is addressed through Aurora’s architecture and capacity planning; for many workloads, scaling reads is the dominant elasticity need.)

Option A only mentions configuring storage scaling, which does not address compute scaling during load increases; RDS storage autoscaling helps prevent running out of space but does not automatically add compute capacity in response to demand. Option B is incorrect because Amazon Redshift is a data warehouse designed for analytics/OLAP, not a transactional MySQL OLTP replacement, and mysqldump to Redshift does not preserve application compatibility. Option D changes the data model by migrating to DynamoDB, which is not MySQL-compatible and would require application redesign—violating the compatibility requirement.

Therefore, C best satisfies compatibility and automatic scaling needs while using managed services that reduce operational overhead during and after migration.

AWS guidance is to cover steady baseline with commitments (Reserved Instances or Savings Plans) and use EC2 Spot Instances for burst capacity to minimize cost. Spot provides up to 90% discounts and is well-suited to fault-tolerant, queue-based workloads; interruptions are handled by replacing capacity automatically while messages remain durably in SQS. Using only Spot (A) risks capacity gaps; only RIs sized for peak (B) wastes cost during low demand. Option D scales on backlog but uses On-Demand for bursts, which is more expensive than Spot. With C, baseline capacity (RIs) keeps processing continuously (no downtime), and Spot adds cost-efficient throughput during spikes, aligning with Well-Architected cost and reliability patterns for queue workers.

AWS Step Functions supports long-running workflows and error retries, making it ideal for a job composed of many tasks. Integration with EventBridge allows automatic triggering from S3 events. This setup is resilient and supports up to 1-year execution duration.

Aurora I/O-Optimized: This storage configuration is designed to provide consistent high performance for Aurora databases. It automatically scales IOPS as the workload increases, without needing to provision IOPS separately.

Cost-Effectiveness: With Aurora I/O-Optimized, you only pay for the storage and I/O you use, making it a cost-effective solution for applications with varying and unpredictable I/O demands.

Implementation:

During the creation of the Aurora PostgreSQL Serverless v2 cluster, select the I/O-Optimized storage configuration.

The storage system will automatically handle scaling and performance optimization based on the application load.

Operational Efficiency: This configuration reduces the need for manual tuning and ensures optimal performance without additional administrative overhead.

To authenticate users using an on-premises Active Directory Federation Service (AD FS), AWS provides the AWS Directory Service AD Connector. AD Connector is a proxy that connects AWS applications to your on-premises Microsoft Active Directory without requiring complex directory synchronization or the need to set up a separate directory in the cloud.

By integrating AD Connector with the Application Load Balancer (ALB), you can authenticate and authorize users using your existing on-premises credentials. This setup allows the ALB to leverage the authentication capabilities of your on-premises AD FS, providing a seamless and secure user experience.

Option Ais the most automated and cost-efficient solution for exporting data to S3 for analytics.

Option Binvolves manual setup of Streams to S3.

Options C and Dintroduce complexity with EMR.

Amazon S3 Storage Lens:

S3 Storage Lens provides organization-wide visibility into object storage usage and activity trends.

It can generate metrics and insights about your S3 buckets, including versioning status.

Configuration:

Enable S3 Storage Lens at the organization level.

Configure the dashboard to include the versioning status metric.

Identify Non-Versioned Buckets:

Use the S3 Storage Lens dashboard to filter and identify buckets that do not have versioning enabled.

Storage Lens provides detailed insights and reports which can be used to enforce compliance and manage storage effectively.

Operational Efficiency: Using S3 Storage Lens provides a centralized, easy-to-use interface for monitoring bucket configurations across multiple Regions and accounts, reducing the need for custom scripts or manual checks.

Amazon DynamoDB supports Time to Live (TTL), which automatically and asynchronously deletes expired items based on a timestamp attribute. TTL is ideal for automatic data expiration with very low operational overhead.

In this scenario:

When a user requests deletion, the system can calculate an expiration timestamp one month in the future.

A Lambda function is used once per request to write or update the item’s TTL attribute to that timestamp (Option C).

DynamoDB TTL then automatically removes the item after the expiration time. Deletion typically occurs within 48 hours of the TTL timestamp, which satisfies the “within one month” requirement.

This approach offloads the actual deletion to DynamoDB and avoids building complex orchestration or periodic cleanup jobs.

Options A, B, and D add unnecessary components (EventBridge, streams, AWS Config, and Lambda-based deletion workflows) on top of TTL or custom logic, increasing operational overhead without providing additional value for the given requirement.

AWS Step Functions is designed to coordinate multiple AWS services into serverless workflows, handling errors, retries, and complex logic. By configuring Amazon S3 Event Notifications to trigger an Amazon EventBridge rule, you can automatically start a Step Functions workflow when a new object is uploaded to S3. Step Functions can manage retries for failed uploads and orchestrate calls to various AWS services with minimal operational effort.

AWS Documentation Extract:

“You can use Amazon EventBridge to initiate AWS Step Functions workflows in response to events from S3 buckets. Step Functions can orchestrate the sequence of AWS service calls and automatically handle retries and errors, making it ideal for automating and managing complex workflows.”

(Source: AWS Step Functions documentation, Using Step Functions with EventBridge and S3)

Other options:

A: Requires manual implementation of orchestration and error handling.

B & C: Involve additional infrastructure management and custom logic for workflow orchestration and error handling, increasing operational effort.

A Network Load Balancer (NLB) supports IP address-based targets, which allows the use of both EC2 and on-premises endpoints. It also supports a static IP address or Elastic IP, which meets the requirement for a fixed IP address needed by some users.

NLB offers high performance, low latency, and minimal operational overhead. Application Load Balancer only supports instance or Lambda targets, not IP addresses outside the VPC. Route 53 Resolver is for DNS, and API Gateway is for HTTP-based APIs, not web applications.

Amazon RDS for Oracle is a managed database service, which significantly reduces operational overhead compared to running Oracle on EC2 or on-premises. AWS Secrets Manager natively integrates with RDS and supports automatic, scheduled password rotation with minimal setup. You can configure the rotation schedule (including yearly), and Secrets Manager will handle the secure password storage and rotation workflow for you.

AWS Documentation Extract:

" AWS Secrets Manager helps you protect access to your applications, services, and IT resources without the upfront investment and on-going maintenance costs of operating your own infrastructure. You can configure automatic rotation for supported databases such as Amazon RDS for Oracle. "

(Source: AWS Secrets Manager documentation)

A, C, D: These solutions require custom scripting, Lambda, and alarms, leading to more operational overhead.

Since the application uses long-lived TCP connections and must remain idle for up to 1 hour without timeout, all components involved in the connection path (ALB, GWLB, and firewall) must have their idle timeout values configured to at least 1 hour.

Gateway Load Balancer supports transparent insertion of firewalls, and configuring consistent idle timeouts ensures connections don’t drop mid-session.

Using just one firewall (option B) introduces a single point of failure. Increasing capacity (option D) doesn ' t solve idle timeout issues. Therefore, C provides a resilient and complete configuration.

Amazon EFS provides a fully managed, highly available, and shared file system that can be mounted by instances across multiple Availability Zones. This ensures consistency of files between EC2 instances and avoids replication issues. EBS volumes, in contrast, are AZ-scoped and not designed for multi-instance sharing. Options A and B rely on custom replication or manual file handling, which increases operational overhead and risks inconsistencies. Option D does not solve the shared access and consistency requirement. By migrating storage to EFS, both EC2 instances will read and write to the same storage system, ensuring that users always access the latest files without 404 errors.

Amazon S3 Access Points simplify managing data access for shared datasets in S3 by allowing the creation of distinct access policies for different applications or users. Each access point has its own policy and can be managed independently. This method avoids overloading a single bucket policy and helps remain within policy size limits.

Option D provides a scalable and operationally efficient solution by offloading individual access controls from a central bucket policy to individually managed access points, which is ideal for environments with many consuming applications.

AWS VPC endpoints (interface and gateway) allow private connectivity from VPC resources to AWS services without requiring public IP addresses or internet gateways. This ensures applications remain isolated in private subnets while securely accessing AWS services. NAT gateways (B) would allow internet access, which does not meet the security requirement. Internet gateways (C) directly expose traffic to the internet, which violates the isolation requirement. Direct Connect (D) connects on-premises environments to AWS but does not provide service access from private subnets. Therefore, option A — using interface VPC endpoints — is the correct solution.

AWS Config has a built-in managed rule (access-keys-rotated) that evaluates IAM access key age. Config rules detect non-compliant resources automatically, without building custom logic.

After Config identifies old keys, EventBridge can trigger an AWS Lambda function to disable and delete the keys. Lambda provides a fully managed compute layer requiring no servers or batch environments.

Using AWS Batch (Options A, B, and D) adds unnecessary operational overhead for a simple automation task.

=====================================================

AWS Organizations: AWS Organizations allows you to create multiple AWS accounts and manage them centrally. You can organize accounts into organizational units (OUs) and apply policies to these units.

Organizational Units (OUs):

Create separate OUs for each department: finance, data analytics, and development.

Place the respective AWS accounts for each department into their corresponding OUs.

Service Control Policies (SCPs):

SCPs are policies that can restrict which AWS services and actions are available to accounts in an OU.

Create SCPs to define which services each department can use and attach these policies to the appropriate OUs.

SCPs apply to all IAM users, groups, and roles within the accounts in the OU, providing centralized control over service usage.

Operational Efficiency: Using AWS Organizations and SCPs provides a scalable and centralized way to manage permissions across multiple accounts with minimal operational overhead.

CloudFront geographic restrictions (also known as geo-blocking) allow you to allow or deny content delivery to specific countries with minimal configuration.

“You can use geo restriction, also known as geoblocking, to prevent users in specific geographic locations from accessing content that you ' re distributing through a CloudFront web distribution.”

— CloudFront Geo Restriction

This is the most operationally efficient approach — no code, no signed URL logic.

Incorrect Options:

A/C: Signed URLs/cookies are for individual access control, not geo-blocking.

D: OAC controls access between CloudFront and S3, not to block specific countries.

The application requires independent scaling, ordered asynchronous processing, and relational data storage. Option A provides a microservices-oriented, decoupled architecture using managed services.

Amazon ECS with AWS Fargate enables containerized workloads without server management and allows each component to scale independently. Amazon RDS supports relational data storage for user profiles and content. Amazon SQS ensures reliable, ordered message processing and decouples long-running background tasks, allowing services to evolve independently.

Option B uses SNS, which does not guarantee message ordering. Option C uses DynamoDB, which does not meet the relational data requirement. Option D offers less granular scaling and higher coupling.

Therefore, A best meets scalability, resilience, and modularity requirements.

Amazon S3 Intelligent-Tiering is designed for unknown or unpredictable access patterns, automatically moving objects to the most cost-effective tier without performance impact.

It is ideal when you do not know object size or access frequency.

S3 Standard (Option B) works but costs more.

S3 One Zone-IA and Glacier Deep Archive (Options D and A) are not appropriate because data is frequently accessed for processing and persists only 24 hours.

=====================================================

Aurora PostgreSQL supports the pgvector extension, which allows storage and querying of vector embeddings directly inside the database. This eliminates the need for external vector databases and provides cost-effective and performant integration for AI workloads.

The best practice to securely provide short-term access to AWS resources, such as Amazon S3, without using long-term credentials, is to use AWS Security Token Service (STS). According to the AWS IAM Best Practices and the Security Pillar of the AWS Well-Architected Framework, temporary credentials should always be used over long-term credentials when possible.

From AWS IAM Documentation:

“Use temporary credentials (IAM roles and AWS STS) instead of long-term access keys. Temporary security credentials are short-term, automatically expire, and are retrieved using AWS STS.”

(Source: IAM Best Practices – IAM User Guide)

Option D outlines the use of a trust relationship and assume-role mechanism via AWS STS. This allows the on-premises application to request temporary, scoped-down credentials to upload files to S3 securely. This approach is:

Secure – Uses short-lived credentials with least privilege

Scalable – No need for EC2 or VPN tunnels

Low Operational Overhead – No infrastructure to maintain

AWS-Recommended – Aligned with security best practices

In contrast:

Option A uses long-term credentials, which is a security risk.

Option B requires additional infrastructure (EC2, VPN), increasing complexity and cost.

Option C relies on IP-based access, which is insecure and not a form of identity-based authentication.

Amazon RDS for MySQL supports read replicas that offload read-intensive workloads such as reporting, leaving the primary instance free for write operations.

“You can use Amazon RDS read replicas to elastically scale out beyond the capacity constraints of a single DB instance for read-heavy database workloads.”

— Working with Read Replicas

This is the recommended approach for minimizing performance impact on the primary DB instance.

The ALB must be in a public subnet to receive internet traffic. The EC2 instances and the RDS database should be in private subnets to prevent direct internet access, minimizing the attack surface. This aligns with AWS security best practices for web application architectures.

Reference Extract:

" Internet-facing ALBs should be placed in public subnets; EC2 instances and RDS databases should be in private subnets to restrict direct internet access. "

Source: AWS Certified Solutions Architect – Official Study Guide, Network Security and Design section.

For the migration of data from an on-premises Oracle database to Amazon Aurora PostgreSQL, this solution effectively handles schema conversion, data migration, and ongoing data replication.

AWS Schema Conversion Tool (SCT): SCT is used to convert the Oracle database schema to a format compatible with Aurora PostgreSQL. This tool automatically converts the database schema and code objects, like stored procedures, to the target database engine.

AWS Database Migration Service (DMS): DMS is employed to perform the data migration. It supports both full-load migrations (for initial data transfer) and continuous replication of ongoing changes (Change Data Capture, or CDC). This ensures that any updates to the Oracle database during the migration are captured and applied to the Aurora PostgreSQL database, minimizing downtime.

Why Not Other Options?:

Option A (SCT + DMS full-load only): This option does not capture ongoing changes, which is crucial for a live database migration to ensure data consistency.

Option B (DataSync + S3): AWS DataSync is more suited for file transfers rather than database migrations, and it doesn’t support ongoing change replication.

Option D (Snowball + S3): Snowball is typically used for large-scale data transfers that don’t require continuous synchronization, making it less suitable for this scenario where ongoing changes must be captured.

AWS References:

AWS Schema Conversion Tool- Guidance on using SCT for database schema conversions.

AWS Database Migration Service- Detailed documentation on using DMS for data migrations and ongoing replication.

Protecting an application from layer 7 (application layer) DDoS attacks is best achieved by using AWS WAF (Web Application Firewall), which provides customizable protection against common web exploits including DDoS attacks at the application layer. AWS WAF supports managed rule groups maintained by AWS, which offer robust, tested protections against OWASP top 10 vulnerabilities and common attack patterns without requiring extensive manual rule creation.

While AWS Shield Standard provides basic network-layer DDoS protection automatically at no additional charge, it does not offer application-layer filtering capabilities. Therefore, option A alone is insufficient.

Option B, involving only custom rules, requires significant operational overhead and expertise, whereas AWS managed rules offer a turnkey solution with ongoing updates from AWS security teams.

Option D, using CloudFront in front of the ALB, can provide additional protection benefits such as caching and geographic restrictions, but the question specifically asks for protecting against layer 7 DDoS on the ALB directly. CloudFront plus WAF is a valid enhanced solution, but the direct and recommended answer in AWS official documents is to use AWS WAF managed rules directly with ALB for application-level protection.

To ensure ALB access only via CloudFront, AWS prescribes restricting the origin to traffic from CloudFront. For ALB origins, the standard pattern is to allow inbound to the ALB only from CloudFront edge IP ranges using security groups or ALB listener rules. Network ACLs are coarse and stateless and add operational burden. CloudFront does not have a “VPC origin” object; instead, CloudFront references the ALB DNS name. By limiting the ALB’s security group to CloudFront IP ranges, direct client access to the ALB is blocked while CloudFront remains permitted. This aligns with security best practices to “restrict origin access to only CloudFront” and use SGs for instance/ALB layer controls.

AWS Site-to-Site VPN is a cost-effective way to securely connect your on-premises data center with AWS resources. In this scenario:

Applications in private subnetsrequire access to the API hosted in the on-premises data center.

ASite-to-Site VPN connectionis a secure and cost-efficient option to route traffic between the VPC and on-premises resources.

Transit GatewayandPrivateLinkare not cost-effective for this use case.

NAT Gatewayonly provides internet access for private subnets, which is not suitable for reaching an on-premises resource.

AWS Documentation Reference:

AWS Site-to-Site VPN

The correct answer is B because the company has a predictable baseline workload that will run for at least 1 year , plus a temporary 2-week increase in demand during a holiday period. The most cost-effective approach is to use a long-term discount option for the steady-state workload and a flexible pricing model for the short-term burst capacity.

An EC2 Instance Savings Plan is a strong fit for the existing workload because it provides cost savings for committed EC2 usage while matching a stable, predictable application footprint. Since the company is already using EC2 and the workload pattern is known, this is an efficient commitment for the baseline capacity. For the additional temporary holiday demand, On-Demand Instances are appropriate because the extra capacity is needed only for a short duration. This avoids overcommitting to capacity that will not be used after the holiday period ends.

Option A is less cost-effective because it misses the savings opportunity for the predictable baseline workload. Option C is incorrect because the application is described as stateful , which makes Spot Instances a poor fit due to potential interruption. Option D is incorrect because purchasing a 12-month commitment for both the normal workload and the temporary holiday increase would likely result in paying for excess committed usage after the 2-week peak ends.

AWS cost optimization guidance recommends using discounted commitment models for steady-state usage and On-Demand for short-lived, temporary spikes. Therefore, the best answer is to use an EC2 Instance Savings Plan for the baseline and On-Demand Instances for the seasonal increase.

AWS Secrets Manager is the recommended service for storing database credentials and performing automated rotation. Any Lambda function version or alias can fetch the latest secret value at runtime, ensuring no outdated credentials exist in deployed versions.

Environment variables (Option C) are static per version. Embedding credentials in code (Option B) is insecure and requires redeployment. Parameter Store (Option D) supports rotation but requires more configuration and is not as seamless as Secrets Manager for database credential rotation.

=====================================================

Option Bensures the use of S3 Object Lock and Versioning to meet compliance for immutability. CloudFront enhances performance while a bucket policy ensures secure access.

Option Alacks immutability safeguards.

Option Cintroduces unnecessary complexity.

Option Dmisses out on additional security benefits offered by CloudFront.

Why Option B is Correct:

HTTP Proxy Integration: Passes XML requests directly to the application, which already supports XML.

JSON Conversion: An AWS Lambda function converts JSON requests to XML and calls the application.

API Gateway: Acts as a front end to handle JSON requests and integrates seamlessly with Lambda for the transformation process.

Why Other Options Are Not Ideal:

Option A: Suggests using API Gateway mappings to convert JSON to XML. API Gateway mapping templates are limited in functionality and are not ideal for complex transformations.

Option C and D: Use HTTP custom integration unnecessarily, which adds complexity without additional benefits.

AWS References:

Amazon API Gateway Integration:AWS Documentation - API Gateway Integration

AWS Lambda:AWS Documentation - Lambda

Amazon ECS on AWS Fargate: AWS Fargate is a serverless compute engine for containers that works with Amazon ECS. It allows you to run containers without managing servers or clusters.

Amazon EFS: Amazon Elastic File System (EFS) provides a simple, scalable, fully managed elastic NFS file system for use with AWS Cloud services and on-premises resources. It can be mounted to ECS tasks running on Fargate, allowing containers to access a shared file system with standard file system semantics, including OS-level permissions.

Auto Scalingis the most effective solution for ensuring seamless scalability of a stateless application. Key points:

Dleverages Auto Scaling with a Spot Fleet for cost efficiency and attaches an ALB to distribute traffic.

A and Bdo not provide automated scaling and would require manual intervention to add more instances.

Cchanges the instance type but does not scale out horizontally, which is required here.

AWS Documentation Reference:

Amazon EC2 Auto Scaling

Amazon OpenSearch Service is the AWS-native service for real-time search, analytics, and visualization. Ingesting streaming data with Amazon Kinesis Data Streams ensures scalable and reliable data ingestion. OpenSearch supports indexing and querying the data in near real time. QuickSight can then be integrated for visualization and reporting. Options A and B involve batch processing and are not optimized for real-time search. Option C (EKS + DynamoDB) is not a fit for search workloads, as DynamoDB does not support advanced text search. Option D provides the closest AWS-native equivalent to the company’s existing search-and-visualization architecture.

Amazon RDS Proxy helps manage thousands of concurrent database connections by pooling and reusing them efficiently. It is especially useful for serverless applications like AWS Lambda that can open numerous connections quickly, potentially overwhelming the database. Using RDS Proxy reduces connection management overhead and improves fault tolerance.

Why Option C is Correct:

S3 Access Points: Provide scalable management of access to large datasets with specific permissions for individual prefixes.

Dynamic Prefixes: Access points simplify managing access to a growing number of prefixes without relying solely on a single bucket policy.

Fine-Grained Control: Resource-based permissions on access points enforce prefix-level isolation effectively.

Why Other Options Are Not Ideal:

Option A: Using deny/allow bucket policies introduces complexity and is less scalable for dynamic prefixes.

Option B: Encryption ensures data security but does not address access management.

Option D: Pre-signed URLs are temporary and not suitable for managing access at scale.

AWS References:

Amazon S3 Access Points:AWS Documentation - S3 Access Points

According to the AWS Well-Architected Framework – Security Pillar and the AWS Identity and Access Management (IAM) User Guide, the root user account in an AWS account is extremely powerful and should be protected with strict security measures.

From AWS documentation:

“We recommend that you not use the root user for everyday tasks, even administrative ones. Instead, create IAM users and grant them only the permissions they need. To help protect your AWS account, enable multi-factor authentication (MFA) for the root user.”

(Source: AWS Identity and Access Management User Guide – Securing the Root User)

The correct and recommended action is to create IAM users with specific permissions for daily operations and enable MFA on the root user to provide an additional layer of security. The root user cannot be disabled, so Option A is technically incorrect. AWS also explicitly advises against using root access keys (Option C) or sharing root credentials (Option D), both of which violate the principle of least privilege.

Best practices summarized from AWS official documentation:

Do not use root user for routine tasks

Enable MFA for root user immediately

Create individual IAM users and assign least privilege

Avoid creating or using root user access keys

These recommendations are foundational to securing any new AWS account and are consistently emphasized in the AWS Certified Solutions Architect – Official Study Guide and the AWS Security Best Practices whitepaper.

Explanation (AWS Docs):

Although the question says “before sending it,” AWS best practice for sensitive data is SSE-KMS (Server-side encryption with AWS KMS keys), which gives full key usage auditing. It integrates with AWS KMS and provides compliance-friendly encryption at rest automatically.

“SSE-KMS uses AWS Key Management Service to manage encryption keys. SSE-KMS also provides an audit trail of key usage.”

— Protecting Data Using Server-Side Encryption

Why not D?

Client-side encryption requires custom key management and adds operational overhead. C is simpler and compliant.

Amazon RDS Custom for Oracle: This service allows you to bring your own Oracle Database licenses and provides the flexibility to customize the database settings, making it suitable for applications that require privileged access and third-party database features.

BYOL (Bring Your Own License):

RDS Custom supports the BYOL model, allowing you to use your existing Oracle licenses and comply with licensing requirements.

This helps in leveraging existing investments and reducing migration costs.

Customization and Third-Party Features:

RDS Custom allows for deeper customization of the database environment compared to standard RDS instances.

This makes it possible to support the third-party features that your application relies on without significant changes.

Migration Process:

Use native Oracle tools like Data Pump or RMAN to migrate the database to RDS Custom.

Customize the database settings post-migration to ensure compatibility with third-party features.

Field-level encryption in Amazon CloudFront provides end-to-end encryption for specific data fields (e.g., credit card numbers, social security numbers). It ensures that sensitive fields are encrypted at the edge before being forwarded to the origin, and only the authorized application with the private key can decrypt them.

This adds a layer of protection beyond HTTPS, which encrypts the whole payload but not individual fields. Signed URLs and cookies are for access control, not encryption. Setting HTTPS Only is a good practice but does not satisfy the field-specific encryption requirement.

AWS Config is a service designed to assess, audit, and evaluate the configurations of AWS resources. It continuously monitors and records your AWS resource configurations and allows you to automate the evaluation of recorded configurations against desired configurations. By starting a configuration recorder, AWS Config will capture changes to supported resource types as configuration items—without the need to modify any of the existing resources. This provides a full history of configuration changes and is specifically intended for exactly this use case.

AWS Documentation Extract:

“AWS Config provides a detailed view of the configuration of AWS resources in your AWS account. This includes how the resources are related to one another and how they were configured in the past so you can see how the configurations and relationships change over time.”

“You can start the configuration recorder, which will record the configuration changes of the supported resources in your AWS account.”

(Source: AWS Config documentation, What is AWS Config?)

Other options:

B: CloudFormation drift detection only works for resources created and managed by CloudFormation and requires stacks.

C: Amazon Detective is used for analyzing and investigating security findings, not for resource configuration tracking.

D: AWS Audit Manager is used for automating evidence collection to help with audits, not for tracking resource configurations.

Comprehensive and Detailed Step-by-Step Explanation:

The goal is totransfer 500 GB dailyfrom multiple global locationsquicklyintoa single S3 bucketwhile keeping operational complexity low.

Option A:✅

Turn on S3 Transfer Acceleration on the destination S3 bucket. Use multipart uploads to directly upload site data to the destination S3 bucket.

S3 Transfer Acceleration (S3-TA)allowsfasterglobal uploads by routing traffic throughAmazon CloudFront’s globally distributed edge locations.

Multipart uploadsimprove efficiency bybreaking large filesinto smaller parts, transferring them in parallel.

Low operational complexity: No need for additional resources or manual replication.

Why is this best?It ensureshigh-speed transferswhileminimizing complexity.

For logs that are:

Written and processed within a short period (24 hours)

Accessed quickly for compute/analytics

With unknown object count and size

Amazon S3 Standard is the most appropriate and cost-effective. Intelligent-Tiering is designed for data stored for longer periods (typically 30+ days) with changing access patterns and charges a per-object monitoring and automation fee that becomes inefficient for very short-lived objects.

S3 Glacier Deep Archive and S3 One Zone-IA are optimized for long-term archival or infrequently accessed data with retrieval time or availability constraints that are not suitable for nightly active processing.

A. VPC peering:Creates a fully meshed architecture, which is complex to manage for multiple VPCs.

B. Transit gateway:Simplifies network management by connecting multiple VPCs and on-premises networks via a central hub.

C. PrivateLink:Restricts communication to the application endpoint but may not allow full VPC connectivity.

D. ALB with internet exposure:Not secure or specific to private network communication.

To meet the requirement of controlling key rotation with minimal operational overhead, creating acustomer managed key(CMK) in AWS KMS is the optimal solution. With CMKs, you can define custom key rotation policies, ensuring that you retain control over the key lifecycle, including enabling automatic key rotation every year.

Key AWS features:

Custom Key Management: A customer managed key allows you to control the key policies, lifecycle, and enable key rotation for compliance.

Least Operational Overhead: Using a customer managed key simplifies encryption management while offering more flexibility than AWS managed or owned keys.

AWS Documentation: The AWS Well-Architected Framework recommends customer managed keys for environments where key control and flexibility are required​.

Amazon EMR on EC2 supports “runtime roles (application-scoped IAM roles)” so each application/step assumes its own IAM role with least-privilege S3 access. You enable this via an EMR security configuration by setting “EnableApplicationScopedIAMRole = true.” The EMR core/Task nodes run under the cluster’s EC2 instance profile; therefore the instance profile must be permitted to “sts:AssumeRole” into the defined EMR runtime roles, and each runtime role must trust the instance profile (trust policy principal is the instance profile role). This design limits each job’s S3 scope via role policies and enforces per-job access segregation. Option B reverses the trust (incorrect). Option E trusts the EMR service role (not used to assume runtime roles). Option F is unrelated (encryption in transit). The correct trio is to enable application-scoped roles (A), authorize the instance profile to assume them (C), and configure the runtime roles’ trust relationship to allow that assumption (D).

S3 Object Lock in Compliance Mode prevents objects from being deleted or overwritten for a fixed, specified retention period. Compliance Mode is specifically designed to meet regulatory requirements, ensuring that no user, including the root user, can modify or delete the object during the retention period.

Reference Extract:

" S3 Object Lock in compliance mode protects objects from being deleted or overwritten during the specified retention period, helping meet regulatory retention requirements. "

Source: AWS Certified Solutions Architect – Official Study Guide, S3 Object Lock and Compliance section.

The correct answers are A and D . The requirement is to replace third-party middleware with an AWS-native solution that supports asynchronous communication , exactly-once delivery , and the least infrastructure management . Amazon SQS FIFO queues are the best messaging component because FIFO queues are designed for workloads that require ordered processing and exactly-once message delivery semantics. That makes option D the correct messaging choice.

For the compute layer, AWS Lambda provides the least infrastructure management because it is a fully managed serverless compute service. Lambda removes the need to manage virtual machines, operating systems, or cluster infrastructure. It integrates naturally with queue-based asynchronous architectures and can be invoked to process messages from SQS while scaling automatically based on queue depth and workload demand. That makes option A the best choice for minimizing operational burden.

Option B is incorrect because EC2 instances would require the company to manage instance provisioning, patching, scaling, and availability. Option C is incorrect because Amazon SNS is a pub/sub messaging service and does not provide exactly-once delivery guarantees like SQS FIFO . Option E is incorrect because Amazon EKS introduces Kubernetes management overhead and is not the lowest-management compute option.

AWS best practices recommend using managed messaging and serverless compute to reduce undifferentiated operational work. For a migration that requires asynchronous processing with exactly-once delivery and minimal infrastructure management, the most appropriate architecture is AWS Lambda functions with Amazon SQS FIFO queues .

Dead-letter queues (DLQs) with Amazon SQS allow Lambda functions to offload failed events for later inspection or retry. Using retry logic with exponential backoff ensures resilience and compliance with best practices for fault-tolerant serverless architectures. This guarantees no data is lost due to transient errors.

The best solution to enforce encryption at rest for Amazon EBS volumes is to use anIAM policyto restrict the creation of unencrypted volumes. To automatically identify and remediate unencryptedvolumes, you can useAWS Configrules, which continuously monitor the compliance of resources, andAWS Systems Managerto automate the remediation by encrypting existing unencrypted volumes. This setup requires minimal administrative overhead while ensuring compliance.

Option B (KMS): KMS is for managing encryption keys, but Config and Systems Manager provide a better solution for automatic detection and enforcement.

Option C (Macie): Macie is for data classification and is not suitable for this use case.

Option D (Inspector): Inspector is used for security vulnerabilities, not encryption compliance.

AWS References:

AWS Config Rules

AWS Systems Manager

Amazon ECS with AWS Fargate provides serverless container compute that can scale tasks dynamically in response to demand. This matches the unpredictable scaling requirements of a gaming workload. Aurora Serverless v2 provides on-demand, autoscaling relational database capacity while supporting complex SQL queries. EC2 with Auto Scaling (A) works but requires significant management overhead. Lambda with DynamoDB (B) is not suitable because the workload requires a relational database and long-running processes. ParallelCluster with HPC (D) is designed for batch scientific workloads, not dynamic, interactive gaming. Option C provides the correct balance of elasticity, performance, and managed services for both compute and relational database needs.

Amazon RDS Multi-AZ deployments provide automatic failover for relational databases such as MySQL, ensuring high availability and durability. The feature maintains synchronous replication between a primary DB instance and a standby in a separate Availability Zone. AWS guarantees that failover typically completes within minutes, ensuring an RTO and RPO of less than 2 minutes. Option A requires manual promotion of replicas, which cannot meet the strict RTO/RPO requirement. Option C depends on custom scripts and manual synchronization, introducing operational risk. Option D creates active-active EC2-based databases, which do not provide synchronous replication or automated failover. Therefore, Multi-AZ RDS (B) is the managed, resilient, and operationally efficient solution that meets the business requirements.

The performance issue occurs because reporting queries compete with production traffic on the same primary database instance. The best-practice AWS solution is to offload read-heavy workloads to a separate database endpoint.

Option C adds an Amazon RDS read replica, which asynchronously replicates data from the primary instance. By redirecting reporting queries to the replica endpoint, the primary database can focus on transactional workloads, significantly improving application performance and customer experience.

Read replicas are specifically designed for this use case: scaling read capacity and isolating reporting or analytics queries. This solution requires minimal changes to the reporting job (endpoint update only) and avoids overprovisioning the primary database.

Option A (RDS Proxy) improves connection management but does not reduce query load or isolate reporting traffic. Option B is invalid because Multi-AZ does not scale reads and is not configurable across three AZs for a single instance. Option D increases instance size but does not address the underlying contention between workloads and increases cost unnecessarily.

Therefore, C is the most efficient and scalable solution to minimize reporting impact while maintaining high performance and availability.

Option B:A gateway endpoint for S3 allows traffic to S3 without using public IPs and integrates with route tables.

Option D:Deploying EC2 instances in a private subnet with a NAT gateway enables outbound internet connectivity for other requirements without public IPs.

Option A:Egress-only internet gateways are for IPv6 traffic and do not work for IPv4 in this context.

Option C:Interface endpoints are not required for S3 as gateway endpoints are more suitable and cost-effective.

Option E:A customer gateway is for hybrid connectivity (e.g., on-premises), not suitable for this case.

AWS Documentation References:

VPC Endpoints

Amazon S3 Gateway Endpoints

AWS WAF (Web Application Firewall) is designed to protect public-facing web applications from common web exploits and allows for deep inspection of HTTP and HTTPS requests. By enabling logging on AWS WAF, you can gain insights into traffic flow, request sources, and blocked requests, which improves both security visibility and posture. Integrating AWS WAF logging with Amazon Kinesis Data Firehose allows automatic, near-real-time delivery of log data to Amazon S3 for analysis, reporting, or integration with analytics tools. This solution not only secures the website but also enables comprehensive traffic analysis, satisfying both requirements efficiently.

Reference Extract from AWS Documentation / Study Guide:

" AWS WAF provides detailed logs of web requests, which can be delivered to Amazon S3 via Amazon Kinesis Data Firehose. This logging enables analysis of web traffic and sources, supporting both security and operational monitoring. "

Source: AWS Certified Solutions Architect – Official Study Guide, Security and Compliance section; AWS WAF Developer Guide (Logging and Monitoring).

This solution offers the least operational overhead while meeting the security and global availability requirements:

Amazon CloudFront and S3: Hosting the static frontend on S3 and serving it via CloudFront provides low-latency global distribution, high availability, and security. S3 is a cost-effective and serverless option for hosting static assets, and CloudFront ensures that the application is cached closer to the users, reducing latency globally.

AWS Lambda and API Gateway: Refactoring the microservices to use Lambda functions with API Gateway allows for a fully serverless, scalable, and highly available backend. This reduces the need for managing EC2 instances, as Lambda automatically scales to meet demand and only charges for the actual usage.

RDS for MySQL: Migrating the MySQL database from an EC2 instance to Amazon RDS significantly reduces operational overhead. RDS manages backups, patching, and scaling, and it offers high availability options (e.g., Multi-AZ).

Option AandDinvolve using EC2 Reserved Instances for the database, which requires more operational maintenance than using RDS.

Option Csuggests using a Network Load Balancer with Lambda, which adds unnecessary complexity for this use case.

AWS References:

Amazon S3 and CloudFront Integration

AWS Lambda with API Gateway

Amazon RDS for MySQL

Amazon S3 with Amazon CloudFront:

Amazon S3 provides highly scalable and durable storage for petabytes of data.

Amazon CloudFront, as a content delivery network (CDN), caches frequently accessed data at edge locations to reduce latency. This combination is ideal for storing and accessing engineering drawings.

Incorrect Options Analysis:

Option B: Amazon S3 Glacier Deep Archive is for long-term archival storage, not frequent access.

Option C: Amazon EBS is unsuitable for large-scale, multi-user data access and does not support caching directly.

Option D: AWS Storage Gateway is for hybrid cloud storage, which is unnecessary for a fully cloud-based architecture.

The correct answer is A because the application processes videos for 5–30 minutes , must run multiple jobs in parallel , and should have the least operational overhead . Amazon ECS with AWS Fargate is a managed container solution that removes the need to provision and manage EC2 instances while still supporting longer-running parallel jobs. Using Amazon SQS standard queues decouples the upload event from processing and provides a scalable buffer for incoming work.

With this design, Amazon S3 sends object-created notifications to the SQS queue whenever users upload videos. ECS services or tasks on Fargate can then consume messages from the queue and process videos independently. Auto scaling based on SQS queue depth ensures the system increases task count when more videos arrive and decreases capacity when demand drops. This provides elasticity and efficient parallel processing with minimal infrastructure management.

Option B is incorrect because EC2-based scaling introduces more operational overhead than Fargate. Option C is incorrect because AWS Lambda has a maximum execution duration and is not appropriate for jobs that can run up to 30 minutes. Option D is also incorrect for the same reason; Lambda is not the best fit for this processing duration, and SNS does not provide the same durable queued work pattern as SQS for controlled parallel processing.

AWS best practices recommend using event-driven queues with containerized workers for medium-duration processing jobs that need concurrency and minimal management. Therefore, S3 + SQS + ECS on Fargate with queue-based scaling is the best solution.

Amazon S3 Glacier Flexible Retrieval is a low-cost archival storage class that supports retrieval of data within minutes (expedited access ~1–5 minutes), making it ideal for audit scenarios where occasional, quick access to archived data is required. In contrast, Glacier Deep Archive takes hours to retrieve.

Detailed Explanation:

A. ECS + API Gateway:Overly complex and costly for an on-demand, intermittent workload.

B. EventBridge + SNS:EventBridge schedules are unnecessary for on-demand generation.

C. EKS + API Gateway:Overkill for this use case, with high operational overhead.

D. Lambda + SES:Most cost-effective and efficient solution for generating and emailing reports on demand.

AWS Firewall Manager integrates with AWS Organizations to centrally manage and apply security group policies, AWS WAF rules, and AWS Shield Advanced protections. It automates the propagation of rules across accounts and Regions and can also audit and remediate noncompliant configurations.

AWS Service Catalogallows organizations to centrally manage commonly deployed IT services and offers self-service deployment capabilities to customers. By creatingService Catalog products, the consulting company can package their solutions and tools for easy reuse by customers while maintaining central control over configuration and access. This provides a standardized and automated solution with the least operational overhead for managing and deploying solutions across different customers.

Option A (CloudFormation): CloudFormation templates are useful but don ' t provide the same level of management and user-friendly self-service capabilities as Service Catalog.

Option C (Systems Manager): Systems Manager is more focused on managing infrastructure and doesn ' t offer the same self-service capabilities.

Option D (AWS Config): AWS Config is used for tracking resource configurations, not for deploying solutions.

AWS References:

AWS Service Catalog

The requirements specify capturing unpredictable and sudden spikes in customer activity, integrating easily with other web applications, and including authorization.

Amazon API Gateway with Lambda authorizer provides a secure, scalable entry point with flexible authorization mechanisms including token validation.

Amazon Kinesis Data Firehose is a fully managed service to reliably load streaming data into destinations such as Amazon S3, which fits well for capturing streaming customer activity data.

API Gateway integrates natively with Firehose for direct ingestion.

This combination supports unpredictable traffic, smooth scaling, and simple authorization.

Option B uses Kinesis Data Streams, which requires more management than Firehose and is less optimized for direct API integration. Options A and D use Gateway Load Balancer and ECS containers plus EFS, which add complexity and are less suited for unpredictable traffic with integrated authorization.

A warm pool is an Auto Scaling feature that keeps instances in a pre-initialized state so they can quickly join the active group when scaling is required. This reduces the time needed for instance bootstrapping and makes new capacity available almost instantly. Option A only increases capacity limits but does not address slow boot times. Option C merely extends grace periods without solving the delay. Option D forces overprovisioning, which is wasteful and not aligned with cost optimization. Using a warm pool (B) directly addresses the problem by reducing response time to scaling events.

Aurora Global Database is designed for low-latency cross-Region reads and disaster recovery for relational data.

Amazon S3 Cross-Region Replication (CRR) automatically replicates unstructured data to another Region, ensuring high availability and resilience.

“Aurora Global Database replicates your data with typical latency of less than 1 second to secondary AWS Regions.”

“Amazon S3 Cross-Region Replication automatically replicates every object uploaded to your bucket to a destination bucket in another AWS Region.”

— Aurora Global Database

— S3 Cross-Region Replication

This combination meets the multi-Region, high availability, fault-tolerant requirement for both relational and unstructured data.

Amazon S3 Bucket Keys reduce the cost of AWS KMS API requests by generating a data key at the bucket level instead of individually calling KMS for every object read or written. This approach is particularly effective when workloads, such as ML pipelines, involve reading large numbers of encrypted objects. Switching to AWS managed keys (A) does not reduce the frequency of API calls. Removing encryption (B) would violate compliance/security requirements. Using CloudHSM (C) adds cost and operational burden. Therefore, the correct solution is D — enabling S3 Bucket Keys with SSE-KMS, which significantly reduces decryption costs while maintaining secure encryption.

Requirement Analysis: Need secure, direct connectivity from an on-premises client to an RDS cluster, accessible only when in the office.

VPC with Private Subnets: Ensures the RDS cluster is not publicly accessible, enhancing security.

Site-to-Site VPN: Provides secure, encrypted connection between on-premises office and AWS VPC.

Implementation:

Create a VPC with two private subnets.

Launch the RDS cluster in the private subnets.

Set up a Site-to-Site VPN connection with a customer gateway in the office.

Conclusion: This setup ensures secure and direct connectivity with minimal exposure, meeting the requirement for secure access from the office.

References

AWS Site-to-Site VPN:AWS Site-to-Site VPN Documentation

Amazon RDS:Amazon RDS Documentation

The SELECT INTO OUTFILE S3 feature allows you to export Amazon Aurora MySQL data directly to Amazon S3 with minimal operational overhead. This method is efficient and cost-effective for archiving historical data.

You can configure S3 Lifecycle rules to transition the exported data to lower-cost storage (e.g., S3 Glacier or S3 Standard-IA) and eventually delete it after 5 years.

No need for additional ETL tools like Glue or DataBrew unless complex transformations are required.

Provisioned IOPS SSD (io1 or io2) is designed for I/O-intensive workloads that require low latency and consistent throughput, which is critical for transactional and production databases. It provides predictable performance, unlike General Purpose SSD, which is burst-based.

The best solution to query and filter S3 data directly without downloading the full object is to use Amazon Athena.

Amazon Athena is an interactive query service that lets you use SQL to analyze structured, semi-structured, and unstructured data directly in Amazon S3, without needing to move or transform the data.

It supports formats like CSV, JSON, ORC, Parquet, and Avro and integrates with AWS Glue Data Catalog for schema management.

Athena is serverless, meaning there’s no infrastructure to manage, and it ' s billed per query, which keeps it cost-effective.

Option B (EMR) is heavier and requires managing a cluster.

Option C (API Gateway) is not suited for querying S3 datasets.

Option D (ElastiCache) is a memory store, not a query engine.

???? Reference:

What is Amazon Athena?

The application is described as stateful, which means user sessions or state are likely stored in memory on the application server. When migrating to an Auto Scaling group behind a load balancer, a consistent user experience typically requires session stickiness, so a user’s subsequent requests continue to be routed to the same backend instance that holds their session state (unless the application is refactored to externalize session state, which is not stated here). For HTTP/HTTPS web applications, an Application Load Balancer (ALB) is the correct Elastic Load Balancing choice because it operates at Layer 7 and provides built-in support for cookie-based sticky sessions.

On the database side, Aurora PostgreSQL supports scaling read capacity by adding Aurora Replicas, and Aurora Auto Scaling can automatically adjust the number of replicas based on demand. This is the intended mechanism to scale the database tier for growing read traffic while preserving write consistency through a single writer. “Aurora writers” do not scale horizontally the same way; Aurora provides one writer endpoint at a time (with fast failover), so auto-scaling “writers” is not the right construct for demand-based scaling.

Option C combines the correct load balancer type (ALB) with stickiness and the correct database scaling mechanism (Aurora Replicas with auto scaling). Option A uses an NLB, which is Layer 4 and does not provide the same Layer 7 sticky session behavior expected for a web application pattern. Options B and D incorrectly focus on scaling Aurora writers, which does not address scaling demand patterns as effectively as scaling read replicas.

Therefore, C provides the most consistent user experience for a stateful web tier and enables the database tier to scale using Aurora Replica auto scaling.

A Stored Volume Gateway stores the entire dataset locally on-premises while asynchronously and securely backing up the data to AWS. This meets the requirement for full local access and automatic transfer to AWS.

Cached Volume Gateway (Option C) keeps only frequently accessed data locally; the full dataset is stored in AWS, not on-premises.

Snowball and Snowball Edge (Options A and B) are migration tools, not ongoing backup solutions.

=====================================================

To restrict S3 access to a specific VPC, AWS documentation specifies using a Deny policy with aws:SourceVpc for all unmatched VPCs:

Deny if aws:SourceVpc != vpc-11aabb22

This ensures only that VPC can reach the bucket via a VPC endpoint.

Allow policies (Options B and C) are incorrectly structured or use the wrong condition logic.

Option D limits by account, not VPC, and does not enforce VPC-level access.

=====================================================

Creating a listener rule on theApplication Load Balancer (ALB)to return a maintenance response during the maintenance window is the most straightforward solution with the least operational overhead. The rule can be configured to match all incoming requests and return a custom response, and it can be easily removed once maintenance is complete.

Option A (Aurora table flag): This adds unnecessary complexity for a temporary maintenance response.

Option B and D (SQS or SNS): These options introduce more components than needed for a simple maintenance message.

AWS References:

ALB Listener Rules

Amazon EFS Lifecycle Management enables automatic cost optimization by transitioning files that haven’t been accessed for a defined period (e.g., 7 days) from EFS Standard to EFS Infrequent Access (IA).

“Amazon EFS Lifecycle Management automatically moves files that haven’t been accessed for a set period to the EFS Infrequent Access storage class, reducing storage costs for infrequently accessed files.”

— Amazon EFS Documentation

Key Points:

EFS IA is ideal for files larger than 128 KB and accessed less frequently.

It’s seamless — no code or tools needed.

Meets requirement for cost optimization and high availability.

Incorrect Options:

A: File Gateway adds unnecessary complexity and does not use EFS.

B: Storing files locally breaks shared access and resiliency.

D: Glacier Deep Archive is cold storage — not " readily available. "

Amazon Route 53 supports failover routing policies, which use health checks to route DNS queries to a secondary Region only if the primary endpoint fails. This design ensures only one Region is active for traffic at any given time. This is the recommended architecture for active-passive, multi-Region DR strategies.

AWS Documentation Extract:

“Failover routing lets you route traffic to a primary resource, such as a web server in one Region, and a secondary resource in another Region. If the primary fails, Route 53 can route traffic to the secondary resource automatically.”

(Source: Amazon Route 53 documentation, Routing Policy Types)

A, D: These options do not configure DNS failover for external users.

C: Geolocation routing is for regional distribution, not DR failover.

AWS documentation states that Amazon Macie is the managed service designed to automatically identify and classify sensitive data stored in Amazon S3, including PII and healthcare-related identifiers.

Storing logs in Amazon S3 provides scalable, durable storage, and Amazon Athena can directly query JSON data stored in S3 using SQL.

Amazon Inspector (Option D) is for vulnerability scanning and does not identify sensitive data. DynamoDB with KMS (Option C) cannot detect sensitive information. EBS (Option B) requires custom tooling and does not support serverless SQL querying.

=====================================================

To improve availability and fault tolerance of an Amazon RDS instance, the recommended approach is to configure a Multi-AZ deployment.

Multi-AZ deployments for RDS automatically replicate data to a standby instance in a different Availability Zone (AZ).

If a failure occurs in the primary AZ (due to hardware, network, or power), RDS will automatically failover to the standby instance with minimal downtime, without administrative intervention.

This is an AWS-managed feature and does not require application modification.

It does not provide scalability or load balancing; it ' s designed for high availability and resiliency.

Options A, B, and D are incorrect:

A refers to cross-Region, which is used for disaster recovery, not high availability.

B and D with SQS do not address high availability directly for the RDS instance; queues help decouple systems but do not make a database more resilient.

???? Reference:

Amazon RDS Multi-AZ Deployments

Comprehensive and Detailed 250 to 300 words of Explanation (AWS documentation-based, no links):

Routing requests to the “appropriate microservices” implies Layer 7 (HTTP/HTTPS) routing such as host-based or path-based rules (for example, /customers to one service and /orders to another). In EKS, the most direct and cost-efficient way to provide this style of routing is to use Kubernetes Ingress with the AWS Load Balancer Controller to provision an Application Load Balancer (ALB). An ALB natively supports HTTP/HTTPS request routing features (paths, hosts, headers), which map cleanly to microservices patterns and reduce the need for additional infrastructure components.

A Network Load Balancer (NLB) (Option A) is primarily Layer 4 and is best for TCP/UDP/TLS pass-through and ultra-high performance. It does not provide the same native HTTP path-based routing behavior required to steer requests between microservices based on URL structure, so it often forces you to add another routing layer (like an in-cluster ingress gateway), which can increase cost and operational complexity.

Option C is not an appropriate request routing mechanism; Lambda is compute, not an ingress/routing layer for EKS. Option D (API Gateway) can front services, but for simple microservice routing into EKS it typically adds per-request costs and additional configuration compared with using an ALB that is purpose-built for HTTP load balancing and routing. API Gateway is a great fit for API management features (throttling, usage plans, keys, transformations), but those are not requirements here.

Therefore, B is the most cost-effective and architecturally aligned solution for HTTP routing across EKS-hosted microservices using managed AWS-native ingress integration.

Amazon SQS supports Interface VPC endpoints (AWS PrivateLink), enabling private connectivity from your VPC to SQS without using public IPs, traversing the public Internet, or requiring NAT/IGW. You can restrict access by attaching a queue resource policy that allows only the specific VPC endpoint and denies all other principals/paths, enforcing that all traffic stays on the AWS network. SSE-SQS (B) encrypts data at rest but does not influence network pathing. STS temporary credentials (C) handle authentication/authorization, not routing. VPC Flow Logs (D) are monitoring/visibility and do not prevent public egress. Creating an SQS VPC endpoint and tightening the queue policy satisfies the requirement of no public IP usage while maintaining secure, private access from serverless components in VPC subnets.

AWSDirect Connectis the best solution for establishing a consistent, low-latency connection from an on-premises data center to AWS without using the public internet. Direct Connect offers dedicated, high-throughput, and low-latency network connections, which are ideal for performance-sensitive applications like a trading platform that processes high volumes of market data and stock trades.

Direct Connect provides a private connection to your AWS VPC, ensuring that data doesn ' t traverse the public internet, which enhances both security and performance consistency.

AWS References:

AWS Direct Connectprovides a dedicated network connection to AWS services with consistent, low-latency performance.

Best Practices for High Performance on AWSfor performance-sensitive workloads like trading platforms.

Why the other options are incorrect:

A. AWS Client VPN: While this offers secure connectivity, it ' s over the public internet and is not designed for the low-latency, high-performance needs of a trading platform.

C. AWS PrivateLink: PrivateLink is used for connecting VPCs and services within AWS, but it is not designed for connecting on-premises data centers to AWS.

D. AWS Site-to-Site VPN: Although this provides secure connectivity, it uses the public internet, which can introduce latency and doesn ' t meet the low-latency requirements of the use case.

Amazon API Gateway with CloudFront: API Gateway allows you to create, deploy, and manage APIs, while CloudFront provides a CDN to deliver content with low latency and high transfer speeds.

AWS WAF (Web Application Firewall):

AWS WAF can be configured in CloudFront to protect against common web exploits, including SQL injection and cross-site scripting (XSS).

WAF allows you to create custom rules to block specific attack patterns and can be managed centrally.

Configuration:

Deploy your APIs using Amazon API Gateway.

Set up an Amazon CloudFront distribution in front of the API Gateway.

Configure AWS WAF on the CloudFront distribution to apply security rules.

Operational Efficiency: This solution provides robust protection with minimal operational overhead by leveraging managed AWS services, ensuring that your APIs are secure without extensive custom implementation.

When designing a microservice architecture where each microservice interacts with different AWS services, it ' s essential to follow the principle of least privilege. This means granting each microservice only the permissions it needs to perform its tasks, reducing the risk of unauthorized access or accidental actions.

The recommended approach is to create individualIAM roleswith policies that grant each microservice the specific permissions it requires. Then, these roles should be associated with the EC2 instances that run the corresponding microservice. By doing so, each EC2 instance will assume its specific IAM role, and permissions will be automatically managed by AWS.

IAM roles provide temporary credentials via the instance metadata service, eliminating the need to hard-code credentials in your application code, which enhances security.

AWS References:

IAM Roles for Amazon EC2explains how EC2 instances can use IAM roles to securely access AWS services without managing long-term credentials.

Best Practices for IAMincludes recommendations for implementing the least privilege principle and using IAM roles effectively.

Why the other options are incorrect:

A. Assign an IAM user to each microservice: This requires managing long-term credentials (access keys), which should be avoided. Storing keys in application code is insecure and creates a maintenance burden.

B. Create a single IAM role: This violates the principle of least privilege, as a single role with broad permissions across all services is less secure.

C. Use AWS Organizations: This approach adds unnecessary complexity. Managing permissions at the account level for each microservice is excessive for this use case and doesn ' t adhere to the principle of least privilege.

Option A:The ALB must accept HTTPS traffic from the public internet. Allowing inbound traffic on port 443 from 0.0.0.0/0 enables this functionality.

Option C:The ALB must forward HTTPS traffic to the web application servers on port 443. Outbound traffic for port 443 must be allowed for this communication.

Option E:The ALB must perform health checks on the web application servers over HTTPS on port 8443. Outbound traffic for port 8443 must be allowed for this purpose.

Option B:Allowing all outbound traffic is overly permissive and does not align with the specific requirements.

Option D and F:Inbound traffic to the ALB from the web application instances is unnecessary because the flow of traffic is from the ALB to the web application instances, not vice versa.

AWS Documentation References:

Application Load Balancer Security Groups

Health Checks for ALBs

Amazon GuardDuty includes RDS Protection that “monitors and profiles access to your Amazon Aurora and Amazon RDS databases” to detect threats such as suspicious login attempts, brute-force activity, and anomalous authentication patterns. GuardDuty can be enabled organization-wide in AWS Organizations with a delegated administrator to centralize findings for all member accounts, minimizing operational overhead. Findings include context like source IP, user, and DB instance, and integrate with Amazon EventBridge for alerting and automated response. SCPs (A) enforce or deny API permissions but do not provide detection/analytics. Exporting general logs (C) requires building and maintaining custom parsing/analytics pipelines. CloudTrail (D) records AWS control-plane API calls and does not log database-level login attempts. Therefore, enabling GuardDuty RDS Protection across the org provides the most operationally efficient, managed detection of abnormal failed and incomplete login attempts.

CloudFront Signed URLs: These URLs allow you to provide limited access to content that is being served through an Amazon CloudFront distribution. Signed URLs can be generated to grant time-limited access to premium customers.

Content Restriction:

By using CloudFront signed URLs, you can control access to your media streams and file content stored in S3.

These URLs can be customized with an expiration time, ensuring that access is only available for a specific period, which is useful for scenarios like movie rentals or music downloads.

Security and Flexibility:

Signed URLs ensure that only authenticated users (premium customers) can access the restricted content.

This approach integrates seamlessly with CloudFront and S3, providing an efficient way to manage access controls without additional overhead.

Operational Efficiency: Using CloudFront signed URLs leverages AWS managed services to handle the complexity of access control, reducing the need for custom implementation and maintenance.

Why Option A is Correct:

ElastiCache for Redis: Provides persistent, scalable caching for in-progress transactions and SQL queries. Redis supports data durability and advanced features, making it suitable for transactional workloads.

Integration with Aurora: Easily integrates with the Aurora cluster to improve query performance.

Why Other Options Are Not Ideal:

Option B: CloudFront and S3 are unsuitable for transactional caching. EC2 instance store volumes are ephemeral and lack persistence.

Option C: Memcached does not offer persistence or advanced transactional support, unlike Redis.

Option D: Combining Redis with EC2 instance store is unnecessary; Redis alone meets all caching requirements.

AWS References:

Amazon ElastiCache:AWS Documentation - ElastiCache

AWS Backup supports cross-Region backup for Amazon EFS, allowing automated, scheduled backups and replication to another Region. This managed service simplifies backup management and ensures data resilience without the need for custom scripts or manual processes

Amazon EFSwithMax I/O performance modeis designed for workloads that require high levels of parallelism, such as video processing across multiple EC2 instances. EFS provides shared file storage that can be mounted on both Windows and Linux EC2 instances, and the Max I/O mode ensures the best performance for handling large files and concurrent access across multiple instances.

Option A and B (FSx for Windows File Server): FSx for Windows File Server is optimized for Windows workloads and would not be ideal for Linux instances or high-throughput, parallel workloads.

Option D (EFS General Purpose mode): General Purpose mode offers lower latency but doesn ' t support the high throughput needed for large, concurrent workloads.

AWS References:

Amazon EFS Performance Modes

AWS Control Tower: Provides a managed service to set up and govern a secure, multi-account AWS environment based on AWS best practices. It automates the setup of AWS Organizations and applies security controls (guardrails).

Networking Account:

Create a centralized networking account that includes a VPC with both private and public subnets.

This centralized VPC will manage and control the networking resources.

AWS Resource Access Manager (AWS RAM):

Use AWS RAM to share the subnets from the networking account with the other workload accounts.

This allows different workload accounts to utilize the shared networking resources without the need to manage their own VPCs.

Operational Efficiency: Using AWS Control Tower simplifies the setup and governance of multiple AWS accounts, while AWS RAM facilitates centralized management of networking resources, reducing operational overhead and ensuring consistent security and compliance.

The company is looking for a solution that provides single sign-on (SSO) across multiple AWS accounts while continuing to manage users and groups in their on-premises Active Directory (AD). AWS IAM Identity Center (formerly AWS SSO) is the recommended solution for this type of requirement.

AWS IAM Identity Centerprovides a centralized identity management solution, enabling single sign-on across multiple AWS accounts and other cloud applications. It can integrate with on-premises Active Directory to leverage existing users and groups.

By configuring a two-way forest trust relationship between AWS Directory Service for Microsoft Active Directory and the company ' s on-premises Active Directory, users can be authenticated by their on-premises AD and still access AWS resources through IAM Identity Center. This solution allows centralized management of AWS accounts within AWS Organizations.

The two-way trust allows mutual access between the on-premises AD and the AWS Directory Service. This means that users and groups in the on-premises AD can be used for authentication in AWS IAM Identity Center while maintaining the existing identity management system.

AWS References:

AWS IAM Identity Center Documentation

AWS Directory Service for Microsoft Active Directory Trust Relationships

AWS Directory Service Integration with IAM Identity Center

Why the other options are incorrect:

A. Create an Enterprise Edition Active Directory in AWS Directory Service: This would require setting up a new directory and managing it in AWS, which adds unnecessary overhead. The requirement is to continue using the existing on-premises AD, making this option unsuitable.

C. Use AWS Directory Service and create a two-way trust relationship: While this approach establishes a trust between on-premises AD and AWS Directory Service, it does not address the single sign-on (SSO) requirements across multiple AWS accounts through IAM Identity Center.

D. Deploy an identity provider (IdP) on Amazon EC2: This is more complex than necessary and introduces more management overhead. AWS IAM Identity Center natively supports integration with on-premises Active Directory without requiring a custom IdP.

AWS Fargate with Spot capacity is the most cost-effective and serverless container option for short-duration jobs that are flexible on start time. Since the job is not latency-critical and runs nightly, it is a good candidate for Fargate Spot, which can offer up to 70% cost savings over On-Demand pricing.

The job runs for 1 hour, which exceeds the AWS Lambda maximum execution time (15 minutes). Therefore, Lambda is not suitable. EC2-based solutions involve higher operational overhead and cost, making Fargate Spot the best low-cost, low-maintenance solution.

This solution addresses the scalability needs of the workload while preventing failed processing attempts due to resource constraints.

Amazon S3 event notificationscan be used to trigger a message to an SQS queue whenever a new video is uploaded.

The existing Auto Scaling group of EC2 instances can poll the SQS queue, ensuring that the EC2 instances only process videos when there is a job in the queue.

SQS decouplesthe video upload and processing steps, allowing the system to handle sudden spikes in video uploads without overloading EC2 instances.

The use ofAuto Scalingensures that the EC2 instances can scale in or out based on the demand, maintaining cost efficiency while avoiding processing failures due to insufficient resources.

AWS References:

S3 Event Notificationsdetails how to configure notifications for S3 events.

Amazon SQSis a fully managed message queuing service that decouples components of the system.

Auto Scaling EC2explains how to manage automatic scaling of EC2 instances based on demand.

Why the other options are incorrect:

A. AWS Lambda functions: While Lambda can handle some workloads, video processing is often resource-intensive and long-running, making EC2 a more suitable solution.

B. Using SNS with Lambda: Similar to A, Lambda is not ideal for large-scale video processing due to its time and memory limitations.

D. AWS Step Functions: While a valid orchestration solution, this introduces more complexity and overhead compared to the simpler SQS-based solution.

Option Auses an IAM role attached to the EC2 instance profile, enabling secure and automated access to Secrets Manager. This is the recommended approach.

Option Buses IAM users, which is less secure and harder to manage.

Option Cis not practical for accessing secrets programmatically.

Option Dviolates best practices by granting direct access to the EC2 instance.

SSE-KMS (Server-side encryption with AWS Key Management Service) not only encrypts data at rest but also integrates with AWS CloudTrail to provide detailed logs of key usage — meeting the audit requirement.

“SSE-KMS provides the ability to audit key usage to see who used the key and when, via AWS CloudTrail.”

— Amazon S3 Encryption Documentation

Benefits:

Encryption with customer-managed or AWS-managed KMS keys

Audit trails of key usage events

Fine-grained access control

Incorrect Options:

A: SSE-S3 does not support auditing of key usage.

C: SSE-C does not integrate with CloudTrail or KMS.

D: Self-managed keys require external key infrastructure and custom audit logging.

The requirement has two parts: (1) continuously or regularly evaluate IAM access key age against a 90-day policy, and (2) remediate noncompliant keys by disabling and deleting them. The least operational effort generally comes from using managed compliance evaluation and lightweight serverless remediation.

AWS Config is designed to assess resource configuration compliance over time. Using an AWS Config rule to check access key age fits the governance model because Config maintains a compliance history, supports central reporting, and can evaluate keys across an account (and commonly across an organization with aggregation patterns). After detection, remediation should be automated with minimal infrastructure, and AWS Lambda is the lightest operational tool for directly calling IAM APIs (UpdateAccessKey to Inactive, then DeleteAccessKey) on a schedule.

Option C pairs these appropriately: Config performs compliance evaluation, and an EventBridge scheduled rule triggers a Lambda function to remediate keys older than 90 days. This avoids running and maintaining compute fleets or batch infrastructure. It also provides clear separation of duties: Config for detection and evidence, Lambda for corrective action.

Options A, B, and D rely on AWS Batch, which introduces additional operational overhead (compute environments, job definitions, queues, scaling, and monitoring) that is unnecessary for a simple IAM housekeeping task. Also, EventBridge by itself is not a compliance evaluation service; it can schedule a job, but it does not inherently track “key age” state or compliance posture the way Config does.

Therefore, C best meets the requirement with the least operational effort by using Config for continuous compliance visibility and a scheduled Lambda for automated key deactivation and deletion.

Amazon S3 Standard-IA: This storage class is designed for data that is accessed less frequently but requires rapid access when needed. It offers lower storage costs compared to S3 Standard while still providing high availability and durability.

Access Patterns: Since the files are frequently accessed in the first 30 days and rarely accessed afterward, transitioning them to S3 Standard-IA after 30 days aligns with their access patterns and reduces storage costs significantly.

Lifecycle Policy: Implementing a lifecycle policy to transition the files to S3 Standard-IA ensures automatic management of the data lifecycle, moving files to a lower-cost storage class without manual intervention. Deleting the files after 4 years further optimizes costs by removing data that is no longer needed.

AWSSecrets Manageris the best solution for securely storing and managing sensitive information, such as usernames and passwords. Secrets Manager provides automatic rotation, fine-grained access control, and encryption of credentials. It is designed to integrate easily with other AWS services, such as CloudFormation, to automate the retrieval of secrets via theBatchGetSecretValue API.

Secrets Manager has a lower operational overhead than manually managing credentials, and it offers features like automatic secret rotation that reduce the need for human intervention.

Option A and C (Parameter Store): While Systems Manager Parameter Store can store secrets, Secrets Manager provides more specialized capabilities for securely managing and rotating credentials with less operational overhead.

Option B and C (AWS Batch): Introducing AWS Batch unnecessarily complicates the solution. Secrets Manager already provides simple API calls for retrieving secrets without needing an additional service.

AWS References:

AWS Secrets Manager

Secrets Manager with CloudFormation

Gateway Endpoint for Amazon S3: A VPC endpoint for Amazon S3 allows you to privately connect your VPC to Amazon S3 without requiring an internet gateway, NAT device, VPN connection, or AWS Direct Connect connection.

Provisioning the Endpoint:

Navigate to the VPC Dashboard.

Select " Endpoints " and create a new endpoint.

Choose the service name for S3 (com.amazonaws.region.s3).

Select the appropriate VPC and subnets.

Adjust the route tables of the subnets to include the new endpoint.

Update Route Tables: Modify the route tables of the subnets to direct traffic destined for S3 to the newly created endpoint. This ensures that traffic to S3 does not go through the NAT instance, avoiding the saturated network and eliminating timeouts.

Operational Efficiency: This solution minimizes operational overhead by removing dependency on the NAT instance and avoiding internet traffic, leading to more stable and secure S3 interactions.

Amazon API Gateway supportsresource policies, which allow you to control access to your API by specifying the IP addresses or ranges that can access the API. By creating a resource policythat explicitly denies access to any IP address outside the allowed set, you can ensure that only trusted IP addresses (such as those from your internal network) can access the critical information in your API. This approach provides fine-grained access control without the need for additional infrastructure or complex configurations.

Option A (Private integration): API Gateway private integrations are for creating private APIs that are only accessible within a VPC, but this solution is about restricting access to certain IP addresses.

Option C (Private subnet and ACLs): Deploying the API in a private subnet and using network ACLs adds unnecessary complexity and isn ' t the best fit for HTTP APIs.

Option D (Security group): API Gateway doesn ' t have a security group because it isn ' t a resource inside a VPC. Instead, resource policies are the correct mechanism for controlling IP-based access.

AWS References:

Controlling Access to API Gateway with Resource Policies

A. Edge-optimized API + Caching:Reduces latency by using Amazon CloudFront for edge locations and enables caching for dynamic content. Compression reduces data transfer latency.

B. Regional API + Caching:Increases latency for global users due to the lack of edge locations.

C. Edge-optimized API + Reserved Concurrency:Reserved concurrency ensures Lambda availability but does not address latency for dynamic content.

D. Regional API + Reserved Concurrency:Lacks edge optimization, increasing latency for global users.

Option B: FSx for Lustre is designed for HPC workloads with high IOPS.

Option E: A cluster placement group ensures low-latency networking for HPC analytics workloads.

Option A: Amazon EFS is not optimized for HPC.

Option D: Mountpoint for S3 does not meet high IOPS needs.

Creating aVPC endpointfor S3 and configuring abucket policyto allow access only from the endpoint ensures that only EC2 instances within the VPC can access the S3 bucket. This solutionimproves security by restricting access at the network level without the need for public internet access.

Option A (IAM policies): IAM policies alone cannot restrict access based on the network location.

Option B and D (Encryption): Encryption secures data at rest but does not restrict network access to the bucket.

AWS References:

Amazon S3 VPC Endpoints

To securely access AWS services like S3 and Secrets Manager from a private VPC without using public IPs, interface VPC endpoints are required. These endpoints are accessible via private IP addresses. For the application to reach these endpoints, appropriate routes must be configured in the route table.

AWS App2Container is a command-line tool that helps containerize existing Java and .NET applications running on-premises or on virtual machines, with minimal refactoring. By using Amazon EKS, the company benefits from a managed Kubernetes service, which significantly reduces the operational overhead compared to managing Kubernetes on EC2.

Amazon RDS for SQL Server provides a fully managed SQL Server database engine with automated backups, patching, and high availability through Multi-AZ deployments. This eliminates the need for the company to manage database infrastructure and software manually.

Overall, option A provides the most streamlined and managed approach for both the application and database layers with the least operational effort.

SQS Standard queues provide at-least-once delivery; they can, by design, deliver duplicate messages, and you cannot turn that off.

To guarantee no duplicates to consumers, AWS provides SQS FIFO queues with exactly-once processing semantics in conjunction with deduplication.

The minimal change to achieve this behavior is to:

Recreate the queue as a FIFO queue and

Enable content-based deduplication, so SQS automatically deduplicates messages with identical bodies within the deduplication window.

Why others are not correct:

A: Long polling reduces empty responses and costs but does not eliminate duplicates.

C: Content-based deduplication is available only for FIFO queues, not for standard queues—so you cannot “modify” a standard queue this way.

D: Moving to Amazon MQ is a much bigger architectural change and adds more operational overhead; it’s not the “fewest changes” approach.

The requirement is to restrict actions by Region to prevent accidental cross-Region operations that could violate data residency rules. The most direct and flexible way to enforce Region-based restrictions in AWS is to use IAM condition keys, specifically aws:RequestedRegion, in identity-based policies (and commonly in permission boundaries or SCPs when managing an organization).

Option B is correct because it applies Region constraints at authorization time. By adding conditions that allow actions only when aws:RequestedRegion matches an approved list (or denies when it matches disallowed Regions), the company can prevent users and roles from creating, modifying, or accessing resources in the wrong Region. This approach is broad and can be applied across many services, not just EC2, making it suitable for enforcing data residency boundaries across a multi-Region footprint.

Option A is too narrow because it restricts only ec2:RunInstances. Data residency concerns typically apply to many services (S3, RDS, DynamoDB, KMS, etc.), and limiting only EC2 does not prevent accidental data operations elsewhere. Option C controls where requests come from (source IP), not which Region is targeted. Option D (AWS Config) is detective, not preventative; it can alert after noncompliant actions occur, which does not meet the requirement to prevent accidental operations.

Therefore, B best meets the requirement by enforcing Region-level guardrails through IAM authorization conditions, helping ensure workloads remain within approved geographic boundaries.

The issue in this case is related to the processing tier, where EC2 instances are overwhelmed at peak times, causing delays.Option D, using anAmazon EC2 Auto Scaling target tracking policybased on theApproximateNumberOfMessagesin the SQS queue, is the best solution.

Auto Scaling with Target Tracking:

Target tracking policiesdynamically scale out or in based on a specific metric. For this use case, you can monitor theApproximateNumberOfMessagesin the SQS queue. When the number of messages (orders) in the queue increases, the Auto Scaling group will scale out more EC2 instances to handle the additional load, ensuring that the queue doesn’t build up and cause delays.

This solution is ideal for handling variable and unpredictable peak times, as Auto Scaling can automatically adjust based on real-time load rather than scheduled times.

Why Not the Other Options?:

Option A (Scheduled Scaling): Scheduled scaling works well for predictable peak times, but this company experiencesunpredictable peak usage, making scheduled scaling less effective.

Option B (ElastiCache for Redis): Adding a caching layer would help if DynamoDB were the bottleneck, but in this case, the issue is CPU overload on EC2 instances in the processing tier.

Option C (CloudFront): CloudFront would help cache static content from the web tier, but it wouldn’t resolve the issue of the processing tier’s overloaded EC2 instances.

AWS References:

Amazon EC2 Auto Scaling Target Tracking

Amazon SQS ApproximateNumberOfMessages

Amazon SQS FIFO queuesensure that orders are processed in the exact order received and maintain message deduplication.

AWS Lambdascales automatically, handling bursts and maintaining high availability in a cost-effective manner.

Option A and D:Amazon SNS does not guarantee ordered processing.

Option C:Standard SQS queues do not guarantee order.

AWS Documentation References:

Amazon SQS FIFO Queues

Export Requirements:

To export data from DynamoDB to Amazon S3, point-in-time recovery (PITR) must be enabled for the tables. This feature creates a snapshot of the data, which is essential for exports.

Incorrect Options Analysis:

Option B: S3 buckets and DynamoDB tables do not need to be in the same region for exports.

Option C: DynamoDB streams are unrelated to the export functionality.

Option D: DAX accelerates reads but has no role in exports.

A VPC gateway endpoint for Amazon S3 enables private connectivity to S3 without routing traffic through a NAT gateway or over the internet, eliminating NAT gateway costs. This solution is secure and redundant, as S3 endpoints are highly available by design.

AWS Documentation Extract:

" A gateway VPC endpoint enables you to privately connect your VPC to supported AWS services without requiring a NAT gateway or internet gateway. "

(Source: Amazon VPC documentation, Gateway Endpoints)

A: NAT instances still incur operational overhead and costs.

B: Internet gateway exposes resources and does not provide private access.

D: Direct Connect is for hybrid networking, not for cost-efficient S3 access.

If the SQS visibility timeout is set shorter than the time it takes for the application to process and delete the message, the message becomes visible to other consumers and can be processed again, resulting in duplicate processing. This is a common cause of duplicate messages when using SQS.

Reference Extract from AWS Documentation / Study Guide:

" If the visibility timeout for a message is set shorter than the time it takes to process the message, the message becomes visible again and can be received and processed again, resulting in duplicate processing. "

Source: AWS Certified Solutions Architect – Official Study Guide, SQS and Messaging section.

Why Option B is Correct:

Amazon SQS: Ensures no requests are lost, even during traffic spikes.

API Gateway: Handles dynamic traffic patterns efficiently, integrating with SQS for asynchronous processing.

Lambda: Polls the queue and processes requests in a serverless and scalable manner.

Dead-Letter Queue (DLQ): Ensures failed messages are retried or logged for debugging.

Why Other Options Are Not Ideal:

Option A: Elastic Beanstalk cannot handle queue-based decoupling, making it unsuitable for spiky traffic.

Option C: ALB to Lambda does not provide buffering for traffic spikes, risking request loss.

Option D: SNS is better suited for notifications, not reliable for ensuring message durability.

AWS References:

Amazon SQS:AWS Documentation - SQS

Amazon RDS read replicas provide a way to offload read traffic from the primary database, allowing read-intensive applications to query the replica without impacting the performance of the production (write) database. This is especially effective for workloads that involve frequently changing data but do not benefit from caching, since queries are rarely repeated.

Reference Extract from AWS Documentation / Study Guide:

" Read replicas allow you to elastically scale out beyond the capacity constraints of a single DB instance for read-heavy database workloads. "

Source: AWS Certified Solutions Architect – Official Study Guide, RDS Read Replica section.

To handle temporary high workloads, Amazon RDS provisioned IOPS can be increased to meet the expected spike in database throughput.

Amazon EFS Elastic throughput mode is ideal for workloads with unpredictable or spiky traffic patterns. It automatically scales throughput based on the file system ' s size and activity, which is more effective than Bursting throughput during high sustained activity like a marketing campaign.

Multi-AZ improves availability but does not directly address performance. Migrating to PostgreSQL during a short campaign period introduces unnecessary complexity.

A & B. GuardDuty:Designed for threat detection, not for identifying or classifying sensitive data in S3 buckets.

C. Macie with EventBridge + SNS:Automatically identifies sensitive data, triggers alerts, and uses SNS for immediate notification via email.

D. Macie with EventBridge + SQS:Introduces latency due to periodic polling and adds unnecessary complexity.

AWS recommends multi-Region active-active architectures for applications requiring high availability, automatic failover, and disaster recovery. Route 53 latency-based routing directs users to the Region providing the lowest latency and automatically shifts traffic if Regional endpoints become unhealthy.

Combined with Application Load Balancers and EC2-based microservices deployed in multiple Regions, this architecture delivers fault tolerance, multi-Region resiliency, and automatic recovery.

Option D provides high availability but does not inherently provide multi-Region failover routing without additional configuration. Option B is single-Region. Option A is not resilient.

=====================================================

AWS Secrets Manager is purpose-built to securely store, manage, and rotate sensitive credentials such as database user names and passwords. Option A is the most operationally efficient solution because it eliminates manual password rotation, reduces human error, and centralizes secret lifecycle management. Secrets Manager integrates natively with Amazon Aurora, enabling automated credential rotation using AWS-managed or custom Lambda rotation logic. Once rotation is enabled, Secrets Manager updates the database credentials and stores the new values securely without requiring administrators to manually update files on EC2 instances.

By assigning IAM permissions to the secret, access can be tightly controlled using least-privilege principles. The application retrieves credentials at runtime, removing the need to store passwords locally on disk, which significantly improves security posture. Secrets Manager also provides auditing capabilities through AWS CloudTrail, allowing visibility into secret access and changes.

Option B (Systems Manager Parameter Store) can securely store secrets, but automated rotation is not natively supported in the same way as Secrets Manager. Implementing rotation with Parameter Store would require additional custom automation, increasing operational complexity. Option C stores credentials in S3, which is not designed for frequent credential rotation or secure secret access patterns, even when encrypted. Option D only encrypts credentials at rest on individual instances and does not address rotation, distribution, or centralized management, resulting in high operational overhead.

Therefore, A best meets the requirements by providing secure storage, automated monthly rotation, fine-grained access control, and minimal operational effort, aligning with AWS security and operational excellence best practices.

The requirement explicitly states that the solution must not involve training a machine learning model, which immediately eliminates options that require custom ML development. Amazon Rekognition is a fully managed computer vision service that can analyze images and detect inappropriate or unwanted content using pretrained models provided by AWS.

Option B correctly uses Amazon Rekognition’s image moderation capabilities to analyze uploaded photos and identify unsafe or unwanted content categories. By invoking Rekognition from an AWS Lambda function, the solution remains serverless, automatically scalable, and operationally efficient. The Lambda function URL provides a secure HTTPS endpoint that the web application can call synchronously during uploads.

Option A violates the requirement because SageMaker Autopilot is designed to build and train ML models. Option C is incorrect because Amazon Comprehend is a natural language processing service for text analysis, not image moderation. CloudFront Functions also do not support calling external AWS services like Rekognition. Option D uses Rekognition Video, which is intended for analyzing video streams, not static images.

Using Lambda and Rekognition together ensures minimal operational overhead, no ML training effort, and immediate enforcement of content moderation policies. The architecture also allows easy extension, such as logging moderation results or blocking uploads based on confidence thresholds.

Therefore, B is the correct solution because it meets the content moderation requirement using AWS-managed ML services without requiring model training.

The correct answer is B because the requirement explicitly calls for a write once, read many (WORM) approach and a mandatory retention period of 1 year . Amazon S3 Object Lock in compliance mode is the AWS feature designed specifically for this purpose. It prevents objects from being deleted or overwritten for a fixed retention period, even by privileged users. That directly satisfies the need to preserve confidential medical results for at least one year after creation.

Using compliance mode is important because it enforces retention in a way that cannot be bypassed by users or administrators. This is appropriate for regulated workloads such as medical records, where strong immutability controls are required. The company can then use IAM policies to allow only a small set of approved users to upload new files while granting other authorized users read-only access.

Option A is incorrect because MFA Delete helps protect against accidental deletion, but it does not provide a full WORM retention model and does not prevent overwrites in the same way as Object Lock. Option C is not sufficient because IAM and bucket policies can be changed by administrators with enough permissions and therefore do not provide the same immutable retention guarantee. Option D is overly complex and does not enforce WORM behavior; tracking object hashes only detects changes after the fact.

AWS best practices for immutable retention on S3 recommend S3 Object Lock when legal hold or retention requirements exist. For least implementation effort and proper WORM enforcement, S3 Object Lock in compliance mode is the most appropriate solution.

The company wants to reduce end-to-end load time for its global customer base.AWS Global Acceleratorprovides a network optimization service that reduces latency by routing traffic to the nearest AWS edge locations, improving the user experience for globally distributed customers.

AWS Global Accelerator:

Global Acceleratorimproves the performance of your applications by routing traffic through AWS’s global network infrastructure. This reduces the number of hops and latency compared to using the public internet.

By creating astandard acceleratorand configuring the existing NLBs as target endpoints, Global Accelerator ensures that traffic from users around the world is routed to the nearest AWS edge location and then through optimized paths to the NLBs in each region. This significantly improves end-to-end load time for global customers.

Why Not the Other Options?:

Option A (ALBs instead of NLBs): ALBs are designed for HTTP/HTTPS traffic and provide layer 7 features, but they wouldn’t solve the latency issue for a global customer base. The key problem here is latency, and Global Accelerator is specifically designed to address that.

Option B (Route 53 weighted routing): Route 53 can route traffic to different regions, but it doesn’t optimize network performance. It simply balances traffic between endpoints without improving latency.

Option C (Additional NLBs in more regions): This could potentially improve latency but would require setting up infrastructure in multiple regions. Global Accelerator is a simpler and more efficient solution that leverages AWS’s existing global network.

AWS References:

AWS Global Accelerator

By usingAWS Global Acceleratorwith the existing NLBs, the company can optimize global traffic routing and improve the customer experience by minimizing latency. Therefore,Option Dis the correct answer.

EBS gp3 volumes allow you to independently configure IOPS and throughput, up to 16,000 IOPS, regardless of the volume size, and at a lower cost compared to io1/io2 provisioned IOPS volumes. This meets the requirement for cost-effective, predictable IOPS performance for Amazon RDS for PostgreSQL.

AWS Documentation Extract:

" With gp3 volumes, you can provision performance independent of storage capacity, up to 16,000 IOPS. This allows for cost-effective scaling for applications that require high performance at a lower price point compared to io1. "

(Source: Amazon EBS documentation, gp3 Volumes)

A: gp2 ties IOPS to volume size; 5 TiB is wasteful if only 15,000 IOPS are needed.

B: io1 works but is significantly more expensive than gp3 for most workloads.

D: Magnetic volumes do not support high IOPS.

Amazon Aurora Serverless is a fully managed, MySQL-compatible database that automatically scales based on demand and provides high availability. Combining this with EC2 Auto Scaling and an Application Load Balancer achieves both application and database high availability and scalability.

Reference Extract:

" Aurora Serverless automatically starts up, shuts down, and scales capacity based on your application ' s needs, providing a cost-effective, highly available database solution. "

Source: AWS Certified Solutions Architect – Official Study Guide, Aurora Serverless and Scaling section.

The key requirements are shared storage, SMB protocol support, and least administrative overhead. Amazon FSx for Windows File Server is a fully managed AWS service designed specifically to provide native SMB file shares compatible with Windows-based workloads and SMB clients.

Option D is the optimal solution because FSx for Windows File Server eliminates the need to manage operating systems, patching, backups, file server clustering, and availability. AWS handles infrastructure management, scaling, and availability, while providing high-performance file storage that integrates seamlessly with Active Directory and supports standard Windows file permissions and SMB semantics. This makes it ideal for media applications that rely on shared file access.

Option A (Storage Gateway Volume Gateway) is designed primarily for hybrid environments that extend on-premises storage into AWS, not for cloud-native shared file systems. Option B (Tape Gateway) is intended for backup and archival workflows, not active file access. Option C requires significant operational overhead, including OS maintenance, security patching, scaling, and high availability design.

Therefore, D meets all requirements with the least administrative effort while providing a scalable, secure, and highly available SMB-compatible storage solution.

The most cost-effective, secure way for private subnets to access AWS services without public IP addresses is to use VPC endpoints:

Interface endpoints (powered by AWS PrivateLink) for services like S3, DynamoDB, etc.

Traffic stays on the AWS network.

“You can privately connect your VPC to supported AWS services using interface VPC endpoints powered by AWS PrivateLink. Instances in your VPC do not require public IP addresses.”

— VPC Endpoints Documentation

Why not others:

NAT gateways incur hourly + data transfer costs and use public IPs.

Internet gateways expose traffic to the internet.

Direct Connect is for private on-prem connectivity, not needed here.

Comprehensive and Detailed 250 to 300 words of Explanation (AWS documentation-based, no links):

The requirement is an automated notification 30 days before an imported ACM certificate expires, delivered to the security team via an existing SNS topic with email subscription. The most operationally efficient approach is to use Amazon EventBridge with the managed event type for ACM certificate expiration. ACM publishes an event when a certificate is approaching expiration, and EventBridge can match that event and route it directly to a target service without custom polling logic.

Option D uses an EventBridge rule for the ACM Certificate Approaching Expiration event and sets the SNS topic as the target. This directly delivers an alert to the existing notification channel (email via SNS) and requires minimal code and minimal ongoing maintenance. It also avoids building and scheduling a scanner that must enumerate certificates, calculate dates, handle pagination, and manage failures.

Option C sends the event to SQS. While SQS is useful for decoupling and buffering, the requirement is to notify the security team, and SNS is already configured for email delivery. Using SQS would add an extra consumer component to read from the queue and publish notifications, which is additional operational overhead.

Options A and B require a custom Lambda-based scanning solution. That introduces scheduling (for example, EventBridge schedule), logic to detect “30 days remaining,” error handling, and ongoing maintenance. Since ACM already emits a purpose-built event for expiring certificates, polling is unnecessary and less efficient.

Therefore, D is the best solution: it uses a native event from ACM, routes it through EventBridge, and notifies the security team through the existing SNS topic with the least operational effort.

Correct Approach:

AWS Security Groups:

Security groups operate at the instance level, making them the ideal tool for controlling access to specific resources such as an Amazon RDS database.

By default, security groups deny all incoming traffic. You can allow access by explicitly specifying another security group.

Associating an RDS database security group with the EC2 instances ' security group ensures only the specified EC2 instances can access the RDS database.

Incorrect Options Analysis:

Option A: Using CIDR blocks for IP-based access is less secure and more difficult to manage. Additionally, Auto Scaling groups dynamically allocate IP addresses, making this approach impractical.

Option B: Network ACLs (NACLs) operate at the subnet level and are stateless. While NACLs can deny or allow traffic, they are not suited to application-specific access control.

Option D: Similar to Option B, using a NACL with CIDR ranges for EC2 IPs is difficult to manage and not application-specific.