SAP-C02 AWS Certified Solutions Architect - Professional Questions and Answers

https://docs.aws.amazon.com/global-accelerator/latest/dg/about-accelerators.alb-accelerator.html Option A is wrong. AWS WAF does not support associating with NLB. https://docs.aws.amazon.com/waf/latest/developerguide/waf-chapter.html Option B is wrong. An ALB does not support an Elastic IP address.https://aws.amazon.com/elasticloadbalancing/features/

"This operation can be called only from the organization's management account. Member accounts can remove themselves with LeaveOrganization instead."https://docs.aws.amazon.com/organizations/latest/APIReference/API_RemoveAccountFromOrganization.html

In the past, EBS can be attached only to one ec2 instance but not anymore but there are limitations like - it works only on io1/io2 instance types and many others as described here. https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ebs-volumes-multi.html EFS has shareable storage In terms of performance, Amazon EFS is optimized for workloads that require high levels of aggregate throughput and IOPS, whereas EBS is optimized for low-latency, random access I/O operations. Amazon EFS is designed to scale throughput and capacity automatically as your storage needs grow, while EBS volumes can be resized on demand.

By creating an AMI that has Grafana pre-installed and storing the existing dashboards in Amazon Elastic File System (Amazon EFS) it allows for faster and more efficient scaling, and by creating an Auto Scaling group that uses the new AMI and setting the Auto Scaling group's minimum, desired, and maximum number of instances to one and creating an Application Load Balancer that serves at least two Availability Zones, it ensures high availability and minimized downtime.

Using AWS CloudFormation to launch a stack with an Application Load Balancer (ALB) in front of an Amazon EC2 Auto Scaling group spanning three Availability Zones, a Multi-AZ deployment of an Amazon Aurora MySQL DB cluster with a Retain deletion policy, and an Amazon Route 53 alias record to route traffic from the company’s domain to the ALB will ensure that

Dis the AWS best practice forsession storage: UseElastiCache for Redis— a fast, in-memory data store that handles high throughput with microsecond latency.

It's highly scalable, fault-tolerant, and optimized for temporary, fast-access session data.

Incorrect:

A: S3 is slow and object-based — not for session I/O.

B: FSx is Windows-only and not ideal for this use case.

C: EBS Multi-Attach has limitations, complexity, and is not suitable for high-performance shared memory.

https://aws.amazon.com/blogs/compute/implementing-canary-deployments-of-aws-lambda-functions-with-alias-traffic-shifting/

The requirement is to test IPv6 connectivity in an AWS VPC. When a VPC is associated with an IPv6 CIDR block, subnets can be configured to assign IPv6 addresses to resources, and routing must be correctly configured to allow IPv6 traffic to flow.

For public IPv6 connectivity, IPv6 traffic must be routed through an internet gateway. Unlike IPv4, IPv6 addresses are globally routable by default, and there is no concept of NAT for IPv6 egress through a NAT gateway. Therefore, for an EC2 instance in a public subnet that needs inbound and outbound IPv6 connectivity, the subnet route table must include a route that sends IPv6 traffic (::/0) to an internet gateway. Option C correctly describes this configuration and is a standard pattern for enabling IPv6 access for public subnets.

For private subnets that require outbound-only IPv6 connectivity (for example, instances that must initiate connections to the internet but must not accept inbound connections), AWS provides an egress-only internet gateway. An egress-only internet gateway allows outbound IPv6 traffic while blocking unsolicited inbound IPv6 traffic, similar in intent to how a NAT gateway is used for IPv4. Option E correctly uses an egress-only internet gateway for IPv6 traffic from a private subnet, making it the correct choice for private IPv6 connectivity testing.

Option A focuses on Direct Connect integration. Although Direct Connect can support IPv6, associating a virtual private gateway with a Direct Connect gateway is not required simply to test IPv6 connectivity to customers worldwide. This option also does not explicitly configure IPv6 internet routing and therefore does not directly meet the stated requirement.

Option B is incorrect because NAT gateways do not support IPv6. NAT gateways are IPv4-only services, so routing IPv6 traffic to a NAT gateway is not a valid configuration.

Option D is also incorrect because NAT instances do not provide a supported or recommended solution for IPv6 traffic. NAT-based designs are intended for IPv4 address translation and are not used for IPv6 connectivity in AWS.

Therefore, using an internet gateway for public IPv6 subnets and an egress-only internet gateway for private IPv6 subnets is the correct and supported approach.

Dis correct:Aurora PostgreSQLis afully managed, high-availabilitydatabase engine.

UseAWS DMSto migrate data andAWS Schema Conversion Tool (SCT)to convert stored procedures from Oracle to Aurora-compatible code.

This meets the requirements ofminimal downtime,managed services, andstored procedure support.

Ais not valid—RDS doesn’t allow custom S3 integration for storage.

Binvolves EC2, which violates the “managed services” constraint.

Crequires complete rewrite of the application logic, which contradicts the minimal downtime requirement.

The best solution is to deploy an Amazon RDS instance with a cross-Region read replica in a secondary Region. This will provide the company with a database solution that can fail over to the secondary Region in case of a disaster. The read replica will have minimal replication lag and can be promoted to become the primary in less than 10 minutes, meeting the RTO requirement. The RPO requirement of less than 5 minutes can also be met by using synchronous replication within the primary Region and asynchronous replication across Regions. This solution will also have the lowest cost compared to the other options, as it does not involve additional services or resources. References: [Amazon RDS User Guide], [Amazon Aurora User Guide]

IAM Access Analyzer helps you identify the resources in your organization and accounts, such as Amazon S3 buckets or IAM roles, shared with an external entity. This lets you identify unintended access to your resources and data, which is a security risk. IAM Access Analyzer identifies resources shared with external principals by using logic-based reasoning to analyze the resource-based policies in your AWS environment.https://docs.aws.amazon.com/IAM/latest/UserGuide/what-is-access-analyzer.html

https://docs.aws.amazon.com/controltower/latest/userguide/controls.html https://docs.aws.amazon.com/controltower/latest/userguide/strongly-recommended-controls.html#ebs-enable-encryption AWS is now transitioning the previous term 'guardrail' new term 'control'.

This option allows the company to use Amazon Aurora MySQL, which is a fully managed relational database service that is compatible with MySQL and offers up to five times better performance than standard MySQL1. By migrating the database to Aurora MySQL, the company can benefitfrom its high availability, durability, scalability, and security features1. By deploying the application in an Auto Scaling group on Amazon EC2 instances behind an Application LoadBalancer, the company can ensure that the application can handle varying levels of traffic and distribute the requests across multiple instances2. By storing sessions in an Amazon ElastiCache for Redis replication group, the company can improve the performance and reliability of the session data by using a fast, in-memory data store that supports replication and failover3.

What is Amazon Aurora?

What is Auto Scaling?

What is Amazon ElastiCache?

The correct answer is B.

B. This solution meets the requirements because it uses AWS Cost Anomaly Detection, which is a feature of AWS Cost Management that uses machine learning to identify and alert on anomalous spend and usage patterns. By configuring a monitor type of AWS Service and applying a filter of Amazon EC2, the solution can track the EC2 usage as a metric across the organization’s accounts.By configuring an alert subscription with a threshold of 10%, the solution can notify the architecture team via email or AmazonSNS if the EC2 usage is more than 10% higher than the average usage for the last 30 days12

A. This solution is incorrect because it uses AWS Budgets, which is a feature of AWS Cost Management that helps to plan and track costs and usage. However, AWS Budgets does not support usage type of EC2 running hours as a budget type. The only supported usage types are Amazon S3 storage, Amazon EC2 RI utilization, and Amazon EC2 RI coverage. Moreover, AWS Budgets does not support setting the budget amount based on the reported average usage from AWS Cost Explorer.The budget amount has to be a fixed or variable value34

C. This solution is incorrect because it uses AWS Trusted Advisor, which is a feature of AWS Premium Support that provides recommendations to follow best practices for cost optimization, security, performance, and fault tolerance. However, AWS Trusted Advisor does not support configuring custom alerts based on EC2 usage or average usage for the last 30 days.The only supported alerts are based on predefined checks and thresholds that are applied to all services and resources in the account56

D. This solution is incorrect because it uses Amazon Detective, which is a service that helps to analyze and visualize security data to investigate potential security issues. However, Amazon Detective does not support configuring EC2 usage anomaly alerts basedon average usage for the last 30 days.The only supported alerts are based on GuardDuty findings and other security-related events that are detected by machine learning models78

The correct solution is to use the authenticate-oidc action for the ALB listener rule and configure it with the details of the AWS Directory Service for Microsoft Active Directory directory. This way, the ALB can use OpenID Connect (OIDC) to authenticate users against the directory and grant them access to the intranet web application. The app client in the directory is used to register the ALB as an OIDC client and provide the necessary credentials and endpoints. The callback URL is the URL that the ALB redirects the user to after a successful authentication. This solution does not require any additional services or roles, and it leverages the existing directory accounts for all users.

The other solutions are incorrect because they either use the wrong action for the ALB listener rule, or they involve unnecessary or incompatible services or roles. For example:

Solution B is incorrect because it uses Amazon Cognito user pool, which is a separate user directory service that does not integrate with AWS Directory Service for Microsoft Active Directory. To use this solution, the company would have to migrate or synchronize their users from the directory to the user pool, which is not required by the question. Moreover, the authenticate-cognito action for the ALB listener rule only works with Amazon Cognito user pools, not with federated identity providers (IdPs) that have metadata from the directory.

Solution C is incorrect because it uses IAM as an identity provider (IdP), which is not compatible with AWS Directory Service for Microsoft Active Directory. IAM can only be used as an IdP for web identity federation, which allows users to sign in with social media or other third-party IdPs, not with Active Directory. Moreover, the authenticate-oidc action for the ALB listener rule requires an OIDC IdP, not a SAML 2.0 federation IdP, which is what IAM provides.

Solution D is incorrect because it uses AWS IAM Identity Center (AWS Single Sign-On), which is a service that simplifies the management of SSO access to multiple AWS accounts and business applications. This service is not needed for the scenario in the question, which only involves a single intranet web application. Moreover, the authenticate-cognito action for the ALB listener rule does not work with external IdPs that use SAML, such as AWS IAM Identity Center.

Authenticate users using an Application Load Balancer

What is AWS Directory Service for Microsoft Active Directory?

Using OpenID Connect for user authentication

https://docs.aws.amazon.com/AmazonS3/latest/userguide/privatelink-interface-endpoints.html#types-of-vpc-endpoints-for-s3

https://aws.amazon.com/premiumsupport/knowledge-center/s3-bucket-access-direct-connect/

Use a private IP address over Direct Connect (with an interface VPC endpoint)

To access Amazon S3 using a private IP address over Direct Connect, perform the following steps:

3. Create a private virtual interface for your connection.

5. Create an interface VPC endpoint for Amazon S3 in a VPC that is associated with the virtual private gateway. The VGW must connect to a Direct Connect private virtualinterface. This interface VPC endpoint resolves to a private IP address even if you enable a VPC endpoint for S3.

This solution will improve the reliability of the application by addressing the issues of scalability, availability, and performance. Containerizing the application will make it easier to deploy and manage on AWS. Migrating the application to an Amazon ECS cluster will allow the application to run on a fully managed container orchestration service. Using the AWS Fargate launch type for the tasks that host the application will enable the application to run on serverless compute engines that are automatically provisioned and scaled by AWS. Creating an Amazon EFS file system for the static content will provide a scalable and shared storage solution that can be accessed by multiple containers. Mounting the EFS file system to each container will eliminate the need to copy the static content to each EBS volume and ensure that the content is always up to date. Configuring AWS Application Auto Scaling on the ECS cluster will enable the application to scale up and down based on demand or a predefined schedule. Setting the ECS service as a target for the ALB will distribute the incoming requests across multiple tasks in the ECS cluster and improve the availability and fault tolerance of the application. Migrating the database to Amazon Aurora MySQL Serverless v2 with a reader DB instance will provide a fully managed, compatible, and scalable relational database service that can handle high throughput and concurrent connections. Using a reader DB instance will offload some of the read load from the primary DB instance and improve the performance of the database.

Migrating the containers that run the application to AWS Fargate for Amazon Elastic Container Service (Amazon ECS) will meet the requirement of not administering the Docker hosting environment. AWS Fargate is a serverless compute engine that runs containerswithout requiring any infrastructure management3. Creating an Amazon Elastic File System (Amazon EFS) file system and adding a volume to the Fargate task definition to point to the EFS file system will meet the requirement of low-latency storage that will automatically scale throughput to meet increased demand. Amazon EFS is a fully managed file system service that provides shared access to data from multiple containers, supports NFSv4 protocol, and offers consistent performance and high availability4. Amazon EFS also supports automatic scaling of throughput based on the amount of data stored in the file system5.

The correct answer is C. This option uses AWS CloudTrail to create a trail in the organization’s management account that applies to all accounts in the organization. This way, the company can centrally manage and audit all API calls to AWS resources across multiple accounts and regions. The company also needs to create a new Amazon S3 bucket with versioning turned on to store the logs. Versioning helps protect against accidental or malicious deletion of log files by keeping multiple versions of each object in the bucket. The company also needs to enable MFA delete and encryption on the S3 bucket to further enhance the security and durability of the data store.

Option A is incorrect because it uses an existing S3 bucket in the organization’s management account to store the logs. This may not be optimal for regulatory compliance, as the existing bucket may have different permissions, encryption settings, or lifecycle policies than a dedicated bucket for CloudTrail logs.

Option B is incorrect because it requires creating a new CloudTrail trail in each member account of the organization. This adds operational overhead and complexity, as the company would need to manage multiple trails and S3 buckets across multiple accounts and regions.

Option D is incorrect because it requires configuring Amazon SNS to send log-file delivery notifications to an external management system that will track the logs. This adds unnecessary complexity and cost, as CloudTrail already provides log-file integrity validation and log-file digest delivery features that can help verify the authenticity and integrity of log files.

"Save your custom error pages in a location that is accessible to CloudFront. We recommend that you store them in an Amazon S3 bucket, and that you don’t store them in the same place as the rest of your website or application’s content. If you store the custom error pages on the same origin as your website or application, and the origin starts to return 5xx errors, CloudFront can’t get the custom error pages because the origin server is unavailable."https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/GeneratingCustomErrorResponses.html

The use of provisioned concurrency ensures the web application’s Lambda functions have pre-initialized execution environments, removing cold start latency and maintaining performance during high-traffic periods.

Route 53 health checks and failover records automate DNS failover to the secondary Region, improving application availability and reliability.

The CloudWatch alarm is configured to monitor the Lambda ConcurrentExecutions metric and send an SNS notification if concurrency usage nears the limit, enabling quick operational response.

This approach minimizes manual management, ensuring reliability and performance during peak traffic while meeting best practices for AWS Lambda and Route 53 failover.

Storing the database credentials in AWS Secrets Manager as a secret that is associated with an AWS Key Management Service (AWS KMS) customer managed key will enable encrypting and managing the credentials securely1. AWS Secrets Manager helps you to securely encrypt, store, and retrieve credentials for your databases and other services2. Attaching a role to each Lambda function to provide access to the secret will enable retrieving the credentials programmatically1. Restricting access to the secret and the customer managed key so that only members of the IT security team’s IAM user group can access them will enable meeting the security requirements1.

The company is building an active-active, multi-Region architecture for an ECS-based web application behind ALBs, with shared container images in ECR and a relational database in Aurora MySQL. The goal is to duplicate the setup in a second Region with minimal operational overhead while allowing minor replication latency.

A is required because ACM certificates are Regional for use with Regional resources like an Application Load Balancer. An ALB in the secondary Region needs an ACM certificate that exists in that same Region to terminate HTTPS. Requesting (or importing) a certificate in the secondary Region and attaching it to the ALB HTTPS listener is the standard managed approach with low operational overhead.

B is not correct because ACM certificates are not shared “across Regions” for direct use by a Regional ALB in another Region. Sharing via AWS RAM is used for sharing resources across accounts within a Region (and for supported resource types), but it does not provide a mechanism to use an ACM certificate issued in one Region directly on an ALB in a different Region.

D is required because the application runs in both Regions and must be able to pull container images locally with minimal operational work. Amazon ECR supports cross-Region replication so images pushed in the primary Region can be replicated automatically to the secondary Region’s ECR repositories. This avoids building custom copy pipelines and reduces the chance of drift. Once replication is enabled, updating images in the primary Region can automatically populate the secondary Region repository.

C is not the best fit. A VPC endpoint for ECR is about private connectivity to ECR within a Region. It does not solve the multi-Region image availability problem by itself, and it introduces the idea of pulling images from the primary Region, which is unnecessary and less optimal when cross-Region replication can keep images local to each Region.

E is required because the database must support multi-Region application operation with tolerable replication latency. Aurora Global Database is the managed feature designed to replicate an Aurora cluster to a secondary Region with low-latency replication and minimal operational overhead compared to custom replication. Enabling write forwarding allows applications in the secondary Region to send write requests to the secondary cluster endpoint, with Aurora forwarding those writes to the primary writer. This reduces application changes compared to forcing all writes to go directly to the primary Region endpoints, and it supports an active-active application deployment pattern where both Regions can accept traffic.

F is not correct because “cross-Region writes between DB clusters” and “multiple writer instances in the primary Region” do not provide a managed, multi-Region, multi-writer Aurora design as described. Aurora’s managed multi-Region pattern is Aurora Global Database with a single primary writer in one Region (with the option to promote a secondary Region during failover) and optional write forwarding from a secondary Region to the primary. The option’s described cross-Region multi-writer behavior is not a standard supported Aurora configuration and would not be the least operational overhead approach.

Therefore, the least operational overhead combination that correctly addresses HTTPS termination in the secondary Region, image availability in both Regions, and managed multi-Region database replication is A, D, and E.

Creating a user pool in Amazon Cognito and configuring it for the application will meet the requirement of giving only a specific list of users the ability to access the application from the internet. A user pool is a directory of users that can sign in to an application with ausername and password1. The company can populate the user pool with the required users and configure the pool to require MFA for additional security2. Configuring a listenerrule on the ALB to require authentication through the Amazon Cognito hosted UI will meet the requirement of not changing the application and not integrating it with an identity provider. The ALB can use Amazon Cognito as an authentication action to authenticate usersbefore forwarding requests to the Fargate service3. The Amazon Cognito hosted UI is a customizable web page that provides sign-in and sign-up functionality for users4.

S3 Event Notifications is a feature that allows users to receive notifications when certain events happen in an S3 bucket, such as object creation or deletion1. Users can configure S3 Event Notifications to invoke an AWS Lambda function when a user stores an image in the bucket. Lambda is a serverless compute service that runs code in response to events and automatically manages the underlying compute resources2. The Lambda function can resize the image in place and store the original file in the same S3 bucket. This way, the solution can handle significant variance in demand and be reliable at enterprise scale. Thesolution can also rerun processing jobs in the event of failure by using the retry and dead-letter queue features of Lambda2.

S3 Lifecycle is a feature that allows users to manage their objects so that they are stored cost-effectively throughout their lifecycle3. Users can create an S3 Lifecycle policy to move all stored images to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. S3 Standard-IA is a storage class designed for data that is accessed less frequently, but requires rapid access when needed4. It offers a lower storage cost than S3 Standard, but charges a retrieval fee. Therefore, moving the images to S3 Standard-IA after 6 months can reduce the storage cost for the solution.

Option A is incorrect because using AWS Step Functions to process the S3 event that occurs when a user stores an image is not necessary or cost-effective. AWS Step Functions is a service that lets users coordinate multiple AWS services into serverless workflows. However, for this use case, a single Lambda function can handle the image resizing task without needing Step Functions.

Option B is incorrect because using Amazon EventBridge to process the S3 event that occurs when a user uploads an image is not necessary or cost-effective. Amazon EventBridge is a serverless event bus service that makes it easy to connect applications with data from a variety of sources. However, for this use case, S3 Event Notifications can directly invoke the Lambda function without needing EventBridge.

Option D is incorrect because using Amazon Simple Queue Service (Amazon SQS) to process the S3 event that occurs when a user stores an image is not necessary or cost-effective. Amazon SQS is a fully managed message queuing service that enables users to decouple and scale microservices, distributed systems, and serverless applications. However, for this use case, S3 Event Notifications can directly invoke the Lambda function without needing SQS. Moreover, storing the resized file in an S3 bucket that uses S3 Standard-IA will incur a retrieval fee every time the file is accessed, which may not be cost-effective for frequently accessed files.

Using Tag Editor to remediate untagged resources is a Best Practice (Page 14 or AWS Tagging Best Practices WhitePaper). However, that is were answer A stops. It doesn't address the requirement of "Management requires cost center numbers and project ID number for all existing and future DynamoDB tables and RDS instances". That is where Answer C comes in and addresses that requirement with SCPs in the company's AWS Organization. AWS Tagging Best Practices -https://d1.awsstatic.com/whitepapers/aws-tagging-best-practices.pdf

B: Outbound Resolver endpoints enable EC2 instances tosendDNS queries to external resolvers (i.e., on-prem DNS servers).

C: A forwarding rule must be created to forward the internal.company.com domain to the on-prem resolver.

E: You must associate the rule withevery VPCthat needs to forward the queries.

Incorrect:

A: Inbound endpoints are forreceivingDNS queries from on-prem to AWS, not vice versa.

D: There’s no such thing as a "System" rule type.

F: Limiting to one VPC won't meet the requirement.

The requirements call for an event-driven redesign that uses serverless services and provides near real-time analytics updates. The current architecture relies on weekly ETL jobs, which is incompatible with near real-time analytics.

A serverless, event-driven architecture on AWS typically uses a managed event bus for decoupling producers and consumers and enabling routing/filtering to multiple targets. The analytics requirement suggests that events (orders, returns, updates) should be streamed to an analytics store continuously rather than copied on a weekly schedule.

Option C aligns with these goals. It modernizes the application components into microservices on a serverless compute substrate (AWS Fargate) and modernizes the databases to managed serverless offerings where possible. Aurora Serverless for MySQL provides an on-demand relational database option that reduces operational overhead for the transactional store. Redshift Serverless provides a managed data warehousing service suitable for analytics without provisioning clusters. Using Amazon EventBridge provides a serverless event routing layer that can deliver events from multiple sources to multiple targets, which supports near real-time propagation of business events from the applications into analytics ingestion and processing.

Option A is not fully aligned: it retains the transactional MySQL database on EC2 (not serverless/managed for the database layer) and moves analytics to Amazon Neptune, which is a graph database, not the typical replacement for an OLAP analytics database. SQS is a queue suited for point-to-point message processing, not a flexible event bus for fan-out to multiple analytics consumers and routing based on event patterns.

Option B is not serverless because it uses EC2 Auto Scaling groups for application compute. Although SNS can distribute messages, this option keeps compute server-based and does not directly provide a near real-time analytics warehouse pattern; it also uses Aurora MySQL for analytics, which is not a typical OLAP warehouse replacement compared to Redshift.

Option D is not appropriate: Amazon AppStream 2.0 is a service for streaming desktop applications to users, not for hosting microservices. AWS IoT Core is optimized for IoT device messaging and telemetry and is not the standard integration backbone for business application event routing across microservices and analytics.

Therefore, option C best satisfies the requirement for an event-driven, serverless architecture with near real-time analytics.

In this solution, the company can use Amazon SES to send email messages, which will minimize operational overhead as SES is a fully managed service that handles sending and receiving email messages. The company can store the email template on Amazon SES with parameters for the customer data and use an AWS Lambda function to call the SendTemplatedEmail API operation, passing in the customer data to replace the parameters and the email destination. This solution eliminates the need to set up and manage an SMTP server on EC2 instances, which can be costly and time-consuming.

https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/custom-tags.htmlhttps://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/configurecostallocreport.html

https://docs.aws.amazon.com/IAM/latest/UserGuide/reference_policies_examples_aws_deny-requested-region.html

https://docs.aws.amazon.com/organizations/latest/userguide/orgs_manage_policies_scps_examples_ec2.html

To offer services to other companies using AWS without traversing the internet, creating a VPC Endpoint Service hosted behind an Application Load Balancer (ALB) and making it available over AWS Direct Connect (DX) is the most suitable solution. This approach ensures that the service traffic remains within the AWS network, adhering to the requirement that connectivity must not traverse the internet. An ALB is capable of handlingHTTP/HTTPS traffic, making it appropriate for web-based services. Utilizing DX for connectivity between the on-premises data center and AWS further secures and optimizes the network path.

AWS Direct Connect Documentation: Explains how to set up DX for private connectivity between AWS and an on-premises network.

Amazon VPC Endpoint Services (AWS PrivateLink) Documentation: Provides details on creating and configuring endpoint services for private, secure access to services hosted in AWS.

AWS Application Load Balancer Documentation: Offers guidance on configuring ALBs to distribute HTTP/HTTPS traffic efficiently.

C: Multipart uploads can leave incomplete parts behind, which incur storage costs. Expiring them after 7 days minimizes waste and saves cost.

E: Since objects are infrequently accessed after 180 days, transitioning toS3 Standard-IAis cost-effective, especially for large files >128 KB (your 100 MB+ files qualify).

Ais for shifting download cost, not reducing your S3 storage expenses.

Bhelps with upload speed butincreases cost.

Dis too aggressive; Glacier is not suited for access patterns within the first few days.

Extend the system with an application tier that uses AWS Step Functions and AWS Lambda. Configure this tier to use Amazon Textract and Amazon Comprehend to perform optical character recognition (OCR) on the forms when forms are uploaded. Store the output in Amazon S3. Parse this output by extracting the data that is required within the application tier. Submit the data to the target system's API. This solution meets the requirements of accurate form extraction, minimal time to market, and minimal long-term operational overhead. Amazon Textract and Amazon Comprehend are fully managed and serverless services that can perform OCR and extract relevant data from the forms, which eliminates the need to develop custom libraries or train and host models. Using AWS Step Functions and Lambda allows for easy automation of the process and the ability to scale as needed.

The data scientists’ EC2 instances in a separate account must access S3 data in a controlled way that satisfies the policy: only authorized networks may access the IoT data. “Authorized networks” in this context means traffic must originate from approved VPCs and must not traverse the public internet.

First, traffic from the EC2 instances to S3 must stay on the AWS network without using public endpoints. A gateway VPC endpoint for S3 in the data scientists’ VPC (option A) allows EC2 instances in that VPC to reach S3 over private connectivity. When using a gateway VPC endpoint, the route tables of the subnets associated with the endpoint automatically route S3 traffic through the endpoint. This ensures that access to S3 is from an authorized network (the VPC) instead of from the public internet.

Second, access must be constrained such that only calls made through the intended S3 access mechanism are allowed. The data lake bucket already has an S3 access point. S3 access points can be restricted by policy and can be referenced in bucket policies through the s3:DataAccessPointArn condition key. By using an S3 bucket policy that allows s3:GetObject only when s3:DataAccessPointArn matches the expected access point ARN (option E), the company can enforce that all valid access uses the approved access point configuration. Combined with the access point’s own network configuration and the VPC endpoint, access is limited to authorized networks.

Option B, blocking public access on the access point, is a good general security practice but does not by itself guarantee that access is restricted to authorized VPC networks. The main enforcement must be through VPC endpoints and bucket/access point policies.

Option C denies s3:GetObject when s3:DataAccessPointArn is a valid access point ARN, which is the opposite of what is required. This would prevent intended access via the access point rather than allow it.

Option D is not correct because gateway VPC endpoints for S3 are configured via endpoint associations with route tables, not by directly routing to an access point in the route table. Traffic to S3 is routed to the VPC endpoint, and the endpoint plus S3 policies determine access.

Therefore, creating a gateway VPC endpoint for S3 in the data scientists’ VPC (option A) and using a bucket policy condition on s3:DataAccessPointArn to allow access only through the authorized access point (option E) together meet the requirement of secure, network-restricted access from authorized networks.

Create AWS Organization:

In the AWS Management Console, navigate to AWS Organizations and create a new organization in the parent account.

Invite LOB Accounts:

Invite each Line of Business (LOB) account to join the organization. This allows centralized management and governance of all accounts.

Enable Consolidated Billing:

Enable consolidated billing in the billing console of the parent account. Link all LOB accounts to ensure a single consolidated invoice that breaks down costs per account.

Apply Service Control Policies (SCPs):

Implement Service Control Policies (SCPs) to define the services and features permitted for each LOB account as per the governance policy, while still delegating full administrative permissions to the LOB accounts.

By consolidating billing and using AWS Organizations, the company can achieve centralized billing and governance while maintaining independent administrative control for each LOB account

Deploying the application on Amazon EKS with managed node groups simplifies the operational overhead of managing the Kubernetes cluster. Running the web servers and application as Kubernetes deployments ensures that the desired number of pods are always running and can scale up or down as needed. Storing the frontend web server session data in an Amazon DynamoDB table provides a fast, scalable, and durable storage option that can be accessed across multiple Availability Zones. Creating an Amazon EFS volume that all applications will mount at the time of deployment allows the application to share data that is frequently accessed between the web and application tiers. References:

https://docs.aws.amazon.com/eks/latest/userguide/managed-node-groups.html

https://docs.aws.amazon.com/eks/latest/userguide/deployments.html

https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/Introduction.html

https://docs.aws.amazon.com/efs/latest/ug/mounting-fs.html

https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/high_availability_origin_failover.html

https://docs.aws.amazon.com/vpn/latest/clientvpn-admin/scenario-peered.html

https://docs.aws.amazon.com/sns/latest/dg/sns-cross-region-delivery.html

https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/UsingWithRDS.IAMDBAuth.html

Option A is correct because placing the EC2 instances behind an Application Load Balancer (ALB) and associating an SSL certificate from AWS Certificate Manager (ACM) with the ALB enables encryption in transit between the client and the ALB. Exporting the SSL certificate and installing it on each EC2 instance enables encryption in transit between the ALB and the web server. Configuring the ALB to listen on port 443 and to forward traffic to port 443 on the instances ensures that HTTPS is used for both connections. This solution achieves end-to-end encryption in transit for the web application12

According to the AWS documentation1, AWS App2Container (A2C) is a command line tool for migrating and modernizing Java and .NET web applications into container format. AWS A2C analyzes and builds an inventory of applications running in bare metal, virtual machines, Amazon Elastic Compute Cloud (EC2) instances, or in the cloud. You can use AWS A2C to generate container images for your applications and deploy them on Amazon ECS or Amazon EKS.

Option A meets the requirements of the scenario because it allows you to migrate your existing Java application to AWS and minimize the administrative overhead to maintain the servers. You can use AWS A2C to analyze your application dependencies, extract application artifacts, and generate a Dockerfile. You can then store your container images in Amazon ECR, which is a fully managed container registry service. You can use AWS Fargate as the launch type for your Amazon ECS cluster, which is a serverless compute engine that eliminates the need to provision and manage servers for your containers. You can grant the ECS task execution role permission to access the ECR image repository, which allows your tasks to pull images from ECR. You can configure Amazon ECS to use an ALB, whichis a load balancer that distributes traffic across multiple targets in multiple Availability Zones using HTTP or HTTPS protocols. You can use the ALB to interact with your application.

Resource-based policies are policies that you attach to a resource, such as an IAM role, to specify who can access the resource and what actions they can perform on it1. Identity-based policies are policies that you attach to an IAM user, group, or role to specify what actions they can perform on which resources2. Trust policies are special types of resource-based policies that define which principals (such as IAM users or roles) can assume a role3.

To allow an IAM user in Account A to assume a role in Account B, the solutions architect needs to do the following:

Configure the resource-based policy on the target role in Account B to allow the action sts:AssumeRole for the IAM user in Account A. This policy grants permission to the IAM user to assume the role4.

Configure the identity-based policy on the user in Account A to allow the action sts:AssumeRole for the target role in Account B. This policy grants permission to the user to perform the action of assuming the role5.

Configure the trust policy on the target role in Account B to allow the principal of the IAM user in Account A. This policy defines who can assume the role.

Resource-based policies

Identity-based policies

Trust policies

Granting a user permissions to switch roles

Switching roles

[Modifying a role trust policy]

Implementing a disaster recovery strategy that minimizes data loss and addresses performance issues involves creating a read replica of the RDS DB instance in a separate region and directing the finance team's queries to this replica. This solution alleviates the performance impact on the primary database. Using Amazon S3 Cross-Region Replication (CRR) ensures that processed documents are available in the disaster recovery region. In the event of a disaster, the read replica can be promoted to a standalone DB instance, and EC2 instances can be launched from pre-created AMIs to serve the web frontend, thereby ensuring resiliency and minimal data loss.

AWS Documentation on Amazon RDS Read Replicas, Amazon S3 Cross-Region Replication, and Amazon EC2 AMIs provides comprehensive guidance on implementing a robust disaster recovery solution. This approach is in line with AWS best practices for high availability and disaster recovery planning.

Deploy AWS DataSync Agent:

Install the DataSync agent on your on-premises environment. This can be done by downloading the agent as a virtual appliance and deploying it on VMware ESXi, Hyper-V, or KVM hypervisors.

Configure Source and Destination Locations:

Set up the source location pointing to your on-premises storage where the software images are currently stored.

Configure the destination location to point to your Amazon S3 bucket in the ap-northeast-1 Region.

Create and Schedule DataSync Tasks:

Create a DataSync task to automate the transfer process. This task will specify the source and destination locations and set options for how the data should be transferred.

Schedule the task to run at intervals that suit your data transfer requirements, ensuring new images are transferred as they are created.

Encryption in Transit:

AWS DataSync automatically encrypts data in transit using TLS, ensuring that your data is secure during the transfer process.

Monitoring and Management:

Use the DataSync console or the AWS CLI to monitor the progress of your data transfers and manage the tasks.

AWS DataSync is an efficient solution that automates and accelerates the process of transferring large amounts of data to AWS, handling encryption, data integrity checks, and optimizing network usage without requiring custom development.

References

AWS Storage Blog on DataSync【40】.

AWS DataSync Documentation【41】.

This option uses the S3 Storage Lens default dashboard to track bucket and encryption metrics across two AWS Regions. S3 Storage Lens is a feature that provides organization-wide visibility into object storage usage and activity trends, and delivers actionable recommendations to improve cost-efficiency and apply data protection best practices. S3 Storage Lens delivers more than 30 storage metrics, including metrics on encryption, replication, and data protection. The default dashboard provides a summary of the entire S3 usage and activity across all Regions and accounts in an organization. The company can give the compliance teams access to the dashboard directly in the S3 console, which requires the least operational overhead.

The best solution is to upload files from the mobile software directly to Amazon S3, use S3 event notifications to create a message in an Amazon Simple Queue Service (Amazon SQS) standard queue, and invoke an AWS Lambda function to perform image processing when a message is available in the queue. This solution will ensure that image processing can scale to handle the load, as Amazon S3 can store any amount of data and handle concurrent uploads, Amazon SQS can buffer the messages and deliver them reliably, and AWS Lambda can run code without provisioning or managing servers and scale automatically based on the demand. This solution will also notify the user when processing is complete by sending a push notification to the mobile app using Amazon Simple Notification Service (Amazon SNS), which is a web service that enables applications to send and receive notifications from the cloud. This solution is more cost-effective than using Amazon MQ, which is a managed message broker service for Apache ActiveMQ that requires a dedicated broker instance, or S3 Batch Operations, which is a feature that allows users to perform bulk actions on S3 objects, such as copying or tagging, but does not support custom code execution. This solution is also more suitable than using Amazon Simple Email Service (Amazon SES), which is a web service that enables applications to send and receive email messages, but does not support push notifications for mobile devices. References: Amazon S3 Documentation, Amazon SQS Documentation, AWS Lambda Documentation, Amazon SNS Documentation

C is correct because the Storage Gateway Volume Gateway in cache mode supportsiSCSI block storage, which is exactly what legacy applications need. It also caches frequently accessed data locally for low-latency performance and integrates with AWS Backup for point-in-time copies.

A is incorrect — tape gateway is designed for archival backup only.

B and D are incorrect because FSx and file gateway offerfile(NFS/SMB) storage, not block/iSCSI support.

Create VPC Endpoint Service:

In the shared VPC, create a VPC endpoint service using the Network Load Balancer (NLB) that fronts the centralized application.

Enable the option to require endpoint acceptance to control which business unit VPCs can connect to the service.

Set Up VPC Endpoints in Business Unit VPCs:

In each business unit VPC, create a VPC endpoint that points to the VPC endpoint service created in the shared VPC.

Use the service name of the endpoint service created in the shared VPC for configuration.

Accept Endpoint Requests:

From the VPC endpoint service console in the shared VPC, review and accept endpoint connection requests from authorized business unit VPCs. This ensures that only authorized VPCs can access the centralized application.

Configure Routing:

Update the route tables in each business unit VPC to direct traffic destined for the centralized application through the VPC endpoint.

This solution ensures secure, private connectivity between the business unit VPCs and the shared VPC, even if there are overlapping CIDR blocks. It leverages AWS PrivateLink and VPC endpoints to provide scalable and controlled access​(AWS Documentation)​​(Amazon Web Services, Inc.)​.

https://aws.amazon.com/about-aws/whats-new/2017/11/announcing-support-for-inter-region-vpc-peering/

CloudFormation cannot delete non-empty S3 buckets. OptionAallows you to create acustom Lambda resourcethat deletes all objects in the S3 bucket before the stack deletes it. The DependsOn ensures the bucket deletion occurs only after the Lambda has completed.

B: Adding DeletionPolicy: Delete does not resolve the issue if the bucket still contains objects.

C: Snapshot doesn't apply to S3 and won't help here.

D: Changing to Amazon EFS would require architectural changes, which are not allowed per requirements.

Create an S3 Bucket:

Log in to the AWS Management Console and navigate to Amazon S3.

Create a new S3 bucket that will serve as the destination for the application data.

Deploy AWS Storage Gateway:

Download and deploy the AWS Storage Gateway virtual machine (VM) on your on-premises environment. This VM can be deployed on VMware ESXi, Microsoft Hyper-V, or Linux KVM.

Configure the File Gateway:

Configure the deployed Storage Gateway as a file gateway. This will enable it to present Amazon S3 buckets as SMB file shares to your on-premises applications.

Create a New File Share:

Within the Storage Gateway configuration, create a new file share that is associated with the S3 bucket you created earlier. This file share will use the SMB protocol, allowing your on-premises applications to access the S3 bucket as if it were a local SMB file share.

Copy Data to the File Gateway:

Use your preferred method (such as robocopy, rsync, or similar tools) to copy data from the on-premises storage to the newly created file gateway endpoint. This data will be stored in the S3 bucket, maintaining accessibility through SMB.

Ensure Secure and Efficient Data Transfer:

AWS Storage Gateway ensures that all data in transit is encrypted using TLS, providing secure data transfer to AWS. It also provides local caching for frequently accessed data, improving access performance for on-premises applications.

This approach allows your existing on-premises applications to continue accessing data via SMB while leveraging the scalability and durability of Amazon S3.

References

AWS Storage Gateway Overview【67】.

AWS DataSync and Storage Gateway Hybrid Architecture【66】.

AWS S3 File Gateway Details【68】.

A is correct because:

App2Containersupports packaging .NET Framework apps into containers without porting to .NET Core.

ECS with EC2gives full control over the OS and patching.

ALBhandles microservice-level load balancing.

RDS for SQL Serverwith Multi-AZ ensures high availability.

Options B, C, and D involve Aurora MySQL (incompatible with SQL Server features) orrequire .NET Core, which involves more significant application changes.

Allows client machines to be able to connect to Session Manager using the AWS CLI instead of going through the AWS EC2 or AWS Server Manager console. https://docs.aws.amazon.com/systems-manager/latest/userguide/session-manager-working-with-install-plugin.htmlhttps://docs.aws.amazon.com/systems-manager/latest/userguide/session-manager-working-with-install-plugin.html#:~:text=aws%20ssm%20start%2Dsession%20%2D%2Dtarget%20instance%2Did

The CloudFront managed prefix list contains the IP ranges for all CloudFront edge locations. By updating the ALB security group ingress to allow access only from this prefix list, the web application will be accessible only by using the CloudFront endpoint. This solution requires the least amount of effort compared to the other options, which involve creating new resources or updating existing ones. This solution also avoids hard-coding IP addresses, which can change over time.

Amazon SageMaker is a fully managed machine learning service that allows users to build, train, and deploy machine learning models quickly and easily1. Users can export their existing TensorFlow models and store the model artifacts in Amazon S3, a highly scalable and durable object storage service2. Users can then deploy the model to Amazon SageMaker and create an endpoint that can be invoked by the web application to provide recommendations3. This way, the solution can leverage the AI/ML capabilities of Amazon SageMaker without having to rewrite the personalization model.

AWS Elastic Beanstalk is a service that allows users to deploy and manage web applications without worrying about the infrastructure that runs those applications. Users can host their Java application in AWS Elastic Beanstalk and configure it to communicate with the Amazon SageMaker endpoint. This way, the solution can reduce the operational overhead of managing servers, load balancers, scaling, and application health monitoring.

AWS Database Migration Service (AWS DMS) is a service that helps users migrate databases to AWS quickly and securely. Users can use AWS DMS to migrate their SQL Server database to Amazon RDS for SQL Server, a fully managed relational database service that offers high availability, scalability, security, and compatibility. This way, the solution canreduce the operational overhead of managing database servers, backups, patches, and upgrades.

Option A is incorrect because using AWS Server Migration Service (AWS SMS) to migrate the on-premises physical application server and the web application VMs to AWS is not cost-effective or scalable. AWS SMS is a service that helps users migrate on-premises workloads to AWS. However, for this use case, migrating the physical application server and the web application VMs to AWS will not take advantage of the AI/ML capabilities of Amazon SageMaker or the managed services of AWS Elastic Beanstalk and Amazon RDS.

Option C is incorrect because using AWS Application Migration Service to migrate the on-premises personalization model and VMs to Amazon EC2 instances in Auto Scaling groups is not cost-effective or scalable. AWS Application Migration Service is a service that helps users migrate applications from on-premises or other clouds to AWS without making any changes to their applications. However, for this use case, migrating the personalization model and VMs to EC2 instances will not take advantage of the AI/ML capabilities of Amazon SageMaker or the managed services of AWS Elastic Beanstalk and Amazon RDS.

Option D is incorrect because containerizing the personalization model and the Java application and using Amazon Elastic Kubernetes Service (Amazon EKS) managed node groups to deploy them to Amazon EKS is not necessary or cost-effective. Amazon EKS is a service that allows users to run Kubernetes on AWS without needing to install, operate, and maintain their own Kubernetes control plane or nodes. However, for this use case, containerizing and deploying the personalization model and the Java application will not take advantage of the AI/ML capabilities of Amazon SageMaker or the managed services of AWS Elastic Beanstalk. Moreover, using S3 Glacier Deep Archive as a storage class for images will incur a high retrieval fee and latency for accessing them.

AWS IoT Core’spre-provisioning hookallows custom logic (like serial number validation)beforea device is registered. This ensures that only installed devices connect. Lambda is ideal for such logic.

AWS IoT Just-in-Time Provisioning

The most effortless way to ensure that all new Amazon Elastic Block Store (EBS) volumes are encrypted at rest is to enable EBS encryption by default in all AWS Regions. This setting automatically encrypts all new EBS volumes and snapshots created in the account, thereby ensuring compliance with encryption policies without the need for manual intervention or additional monitoring.

AWS Documentation on Amazon EBS encryption provides guidance on enabling EBS encryption by default. This approach aligns with AWS best practices for data protection and compliance, ensuring that all new EBS volumes adhere to encryption requirements with minimal operational effort.

Comprehensive and Detailed Explanation:

Option C offers a scalable and low-overhead solution for managing custom controls across multiple AWS accounts:

AWS Control Tower provides a pre-configured environment to set up and govern a secure, multi-account AWS environment based on AWS best practices.

Customizations for AWS Control Tower (CfCT) allows for the deployment of custom configurations and resources, such as AWS Config rules and IAM policies, across accounts and organizational units using AWS CloudFormation templates.

Amazon EventBridge integrates with AWS Control Tower to automate the deployment of customizations during account provisioning events, ensuring that all new accounts adhere to the defined controls without manual intervention.

This approach ensures consistent enforcement of custom controls across all accounts with minimal operational overhead.

https://docs.aws.amazon.com/compute-optimizer/latest/APIReference/API_ExportLambdaFunctionRecommendations.html

A is correct because:

DynamoDB global tablesallow for multi-Region, active-active usage.

RDS MySQL read replicain another Region supports read workloads and can bepromotedduring disaster to act as a standalone DB.

B is incorrect: DAX is a cache, not a replication mechanism.

C is wrong because Multi-AZ doesn’t span Regions.

D is more manual and error-prone.

https://aws.amazon.com/s3/features/access-points/

https://aws.amazon.com/blogs/storage/managing-amazon-s3-access-with-vpc-endpoints-and-s3-access-points/

By splitting the 12 instances across three Availability Zones, the system can maintain high availability and availability of resources in case of a failure. Option D also uses a combination of On-Demand Instances with Capacity Reservations and Spot Instances, which allows for scheduled jobs to be run on the On-Demand instances with guaranteed capacity, while also taking advantage of the cost savings from Spot Instances for the user jobs which have lower SLA requirements.

"A usage plan specifies who can access one or more deployed API stages and methods—and also how much and how fast they can access them. The plan uses API keys to identify API clients and meters access to the associated API stages for each key. It also lets you configure throttling limits and quota limits that are enforced on individual client API keys."https://docs.aws.amazon.com/apigateway/latest/developerguide/api-gateway-api-usage-plans.html

A rate-based rule tracks the rate of requests for each originating IP address, and triggers the rule action on IPs with rates that go over a limit. You set the limit as the number of requests per 5-minute time span...... The following caveats apply to AWS WAF rate-based rules: The minimum rate that you can set is 100. AWS WAF checks the rate of requests every 30 seconds, and counts requests for the prior five minutes each time. Because of this, it's possible for an IP address to send requests at too high a rate for 30 seconds before AWS WAF detects and blocks it. AWS WAF can block up to 10,000 IP addresses. If more than 10,000 IP addresses send high rates of requests at the same time, AWS WAF will only block 10,000 of them. "https://docs.aws.amazon.com/waf/latest/developerguide/waf-rule-statement-type-rate-based.html

https://aws.amazon.com/premiumsupport/knowledge-center/cross-account-access-s3/

https://docs.aws.amazon.com/appsync/latest/devguide/graphql-overview.html

AWS AppSync is a fully managed GraphQL service that allows applications to securely access, manipulate, and receive data as well as real-time updates from multiple data sources1. AWS AppSync supports GraphQL subscriptions to perform real-time operations and can push data to clients that choose to listen to specific events from the backend1. AWS AppSync uses WebSockets to establish and maintain a secure connection between the clients and the API endpoint2. Therefore, using AWS AppSync and leveraging WebSockets is a suitable design to reduce comment latency and improve user experience.

C is correct: AWS Transfer Family can store uploaded files directly into Amazon S3.S3 event notificationscan triggerAWS Glue jobsto transform the data. Upon successful transformation, Glue can send a message toAmazon SQS, enabling event-driven architecture with minimal management.

A and D involve cron jobs or scheduled Glue jobs, which increase operational overhead and delay.

B (EMR) is overkill for frequent, lightweight transformations.

https://aws.amazon.com/migration-evaluator/features/

The company should create a Network Load Balancer (NLB) and associate it with one static IP address in multiple Availability Zones. The company should also create an ALB-type target group for the NLB and add the existing ALB. The company should add the NLB IP addresses to the firewall appliance and update the clients to connect to the NLB. This solution will allow traffic flow to AWS from the on-premises network by using static IP addresses that can be added to the firewall appliance’s allow list. The NLB will forward requests to the ALB, which will use path-based routing to forward requests to the target groups.

S3 Transfer Acceleration is a feature that enables fast, easy, and secure transfers of files over long distances between your client and an S3 bucket1. It works by leveraging the CloudFront edge network to route your requests to S3 over an optimized network path1. By using a Transfer Acceleration endpoint when generating a presigned URL, you can allow authenticated users to upload objects fasterand more reliably2. Additionally, using the S3 multipart upload API can improve the performance of large object uploads by breaking them into smaller parts and uploading them in parallel3.

S3 Transfer Acceleration

Using Transfer Acceleration with presigned URLs

Uploading objects using multipart upload API

Updating the ingestion process to use Amazon Kinesis Data Firehose to save data to Amazon S3 will reduce the need for EC2 instances and EBS volumes for data storage1. Using AWS Batch with Spot Instances to perform nightly processing will leverage the cost savings of Spot Instances, which are up to 90% cheaper than On-Demand Instances2. AWS Batch will also handle the scheduling and scaling of the processing jobs. Setting the maximum Spot price to 50% of the On-Demand price will reduce the chances of interruption and ensure that the processing is cost-effective.

Comprehensive and Detailed in Depth Explanation:

A is correct because AWS CodeBuild is a fully managed build service that supports running tests and security scans. EventBridge can detect test failures and send notifications via SNS. AWS CDK with manifest files is a robust way to manage feature toggles. CodePipeline supports manual approval stages to allow lead developer intervention.

The best solution is to modify the Kinesis Data Firehose configuration and Athena table definition to partition the data by date and time. This will reduce the amount of data scanned by Athena and improve the query performance. Changing the Athena query to view the relevant partitions will also help to filter out unnecessary data. This solution requires minimal operational overhead as it does not involve creating additional resources or changing the log format. References: [AWS WAF Developer Guide], [Amazon Kinesis Data Firehose User Guide], [Amazon Athena User Guide]

The company does not have a data inventory and needs to identify which S3 buckets contain sensitive data. The appropriate AWS managed service for discovering and classifying sensitive data in S3 is Amazon Macie. Macie is designed to discover, classify, and report on sensitive data such as PII in S3 buckets. Amazon Inspector is primarily focused on vulnerability management for compute and container resources and does not provide S3 sensitive data classification in the way Macie does.

After identifying sensitive data locations, the company needs to ensure sensitive data is encrypted with a key that only administrators can access. SSE-S3 uses S3-managed keys and does not provide fine-grained administrative control of key usage in the same way as SSE-KMS with a customer managed key. Using AWS KMS customer managed keys allows the company to control access through key policies and IAM policies so that only designated administrator principals can use or manage the key.

The requirement also implies existing objects already encrypted with SSE-S3 need to be re-encrypted with SSE-KMS for sensitive objects. Changing default encryption only affects new objects. Existing objects must be rewritten (copied over themselves or copied to a new location) using SSE-KMS with the customer managed key. An orchestrated workflow is a common approach to iterate over identified objects and perform copy operations with the desired encryption settings.

Option C uses Macie for discovery, creates a KMS customer managed key restricted to administrators, sets bucket default encryption to SSE-KMS for future objects, and uses a Step Functions workflow to re-encrypt existing sensitive objects. This meets both the discovery requirement and the encryption/control requirement.

Option A is incorrect because Inspector is not the right service to inventory sensitive data in S3. Although the use of a customer managed KMS key and bucket policy enforcement is directionally correct for controlling encryption on writes, the first step (sensitive data discovery) is wrong.

Option B is incorrect because AWS managed keys cannot have their key policies modified by customers in the way customer managed keys can. Also, Inspector is not the right tool for sensitive data discovery in S3.

Option D is incorrect for the same reasons: it relies on Macie correctly for discovery but then attempts to modify an AWS managed key policy, which is not the correct method for restricting access. To restrict access, the company should use a KMS customer managed key with an appropriate key policy.

Therefore, using Amazon Macie plus an AWS KMS customer managed key and a workflow to re-encrypt existing sensitive objects is the correct solution.

The cost of EFS backup cold storage is $0.01 per GB-month, whereas the cost of EFS backup warm storage is $0.05 per GB-month1. Therefore, moving the backups to cold storage as soon as possible will reduce the storage cost. However, cold storage backupsmust be retained for a minimum of 90 days2, otherwise they incur a pro-rated charge equal to the storage charge for the remaining days1. Therefore, setting the backup retention period to 30 days will incur a penalty of 60 days of cold storage cost for each backup deleted. This penalty will still be lower than keeping the backups in warm storage for 7 days and then in cold storage for 83 days, which is the current configuration. Therefore, option A is the most cost-effective solution.

For a development environment that requires frequent resource recreation and connectivity to applications hosted in a shared services account, the most efficient solution involves using AWS Resource Access Manager (RAM) and the transit gateway in the shared services account. By turning on automatic acceptance for the transit gateway in the shared services account and sharing it with the development account through AWS RAM, the development team can easily recreate their connection as needed without manual intervention. This setup allows for scalable, flexible connectivity between accounts while minimizing operational overhead and ensuring consistent access to shared services.

AWS Documentation on AWS Resource Access Manager and Transit Gateway provides guidance on sharing network resources across AWS accounts and enabling automatic acceptance for transit gateway attachments. This approach is also supported by AWS best practices for multi-account strategies using AWS Organizations and network architecture.

https://aws.amazon.com/blogs/storage/new-enhancements-for-moving-data-between-amazon-fsx-for-lustre-and-amazon-s3/

Configuring the Aurora MySQL DB cluster to publish slow query and error logs to AmazonCloudWatch Logs will allow the solutions architect to monitor and troubleshoot the database performance by identifying slow or problematic queries1. CloudWatch Logs also provides features such as metric filters, alarms, and dashboards to analyze and visualize the log data2.

Implementing the AWS X-Ray SDK to trace incoming HTTP requests on the EC2 instances and implement tracing of SQL queries with the X-Ray SDK for Java will allow the solutions architect to measure and map the end-to-end latency and performance of the web application3. X-Ray traces show how requests travel through the application components, such as web servers, load balancers, microservices, and databases4. X-Ray also provides features such as service maps, annotations, histograms, and error rates to analyze and optimize the application performance.

Installing and configuring an Amazon CloudWatch Logs agent on the EC2 instances to send the Apache logs to CloudWatch Logs will allow the solutions architect to monitor and troubleshoot the web server performance by collecting and storing the Apache access and error logs. CloudWatch Logs also provides features such as metric filters, alarms, and dashboards to analyze and visualize the log data2.

Publishing Aurora MySQL logs to Amazon CloudWatch Logs

Working with log data in CloudWatch Logs

Instrumenting your application with the X-Ray SDK for Java

Tracing requests with AWS X-Ray

[Analyzing application performance with AWS X-Ray]

[Using CloudWatch Logs with your Apache web server]

Option B effectively addresses the disaster recovery requirements:

Creating a new backup vault in both the source and recovery accounts allows for organized and secure storage of backups.

Using a multi-Region customer managed KMS key ensures that the backups are encrypted at all times and can be decrypted in the recovery account, maintaining data security during cross-account and cross-Region transfers.

Redirecting backups to the new backup vault in the source account and configuring a key policy to allow access from the recovery account facilitates seamless and secure backup copying.

Scheduling a cross-account backup plan enables automated and regular copying of backups to the recovery account, ensuring that the DR plan is consistently maintained.

This approach ensures that backups are securely and efficiently replicated to the recovery account in a different Region, aligning with the company's DR requirements.

https://aws.amazon.com/kinesis/data-firehose/faqs/

The correct answer is D.

D. This solution meets the requirements because it uses a scheduled scaling policy for the EC2 instances, which can adjust the capacity according to the known sale events. It also uses Amazon Aurora PostgreSQL Multi-AZ DB cluster, which provides high availability and durability for the database. It uses Amazon EventBridge rules and AWS Lambda functions to create a larger Aurora Replica before a sale event and fail over to it, which can improve the performance and handle the increased traffic.It also uses anotherEventBridge rule and Lambda function to scale down the Aurora Replica after the sale event, which can save costs123

A. This solution is incorrect because it uses predictive scaling policy for the EC2 instances, which is not suitable for one-time events that are scheduled. Predictive scaling is based on historical data and machine learning, which may not accurately forecast the demand for sale events. It also uses Amazon Aurora PostgreSQL Serverless v2 Multi-AZ DB instance, which does not support read replicas.The use of AWS Step Functions state machine and Lambda functions to pre-warm the database is unnecessary and adds complexity45

B. This solution is incorrect because it uses Amazon RDS for PostgreSQL Multi-AZ DB instance with automatically scaling read replicas, which may not provide enough performance improvement for the sale events. The use of EventBridge rules and Lambda functions to create a larger read replica and fail over to it is risky and may cause downtime ordata loss.The use of another EventBridge rule and Lambda function to scale down the read replica is also risky and may cause inconsistency or data loss67

C. This solution is incorrect because it uses predictive scaling policy for the EC2 instances, which is not suitable for one-time events that are scheduled. Predictive scaling is based on historical data and machine learning, which may not accurately forecast the demand for sale events.The use of AWS Step Functions state machine and Lambda functions to pre-warm the database is unnecessary and adds complexity45

https://repost.aws/knowledge-center/kinesis-readprovisionedthroughputexceeded

Follow Data Streams best practices

To mitigate ReadProvisionedThroughputExceeded exceptions, apply these best practices:

• Reshard your stream to increase the number of shards in the stream.

• Use consumers with enhanced fan-out. For more information about enhanced fan-out, see Developing custom consumers with dedicated throughput (enhanced fan-out).

• Use an error retry and exponential backoff mechanism in the consumer logic if ReadProvisionedThroughputExceeded exceptions are encountered. For consumer applications that use an AWS SDK, the requests are retried by default.

According to the AWS documentation1, to access a private API from a VPC, you need to do the following:

Create an interface VPC endpoint for API Gateway in your VPC. This creates a private connection between your VPC and API Gateway.

Attach an endpoint policy to the VPC endpoint that allows the execute-api:lnvoke action for your private API. This grants permission to invoke your API from the VPC.

Enable private DNS naming for the VPC endpoint. This allows you to use the same DNS names for your private APIs as you would for public APIs.

Configure a resource policy for your private API that allows access from the VPC endpoint. This controls who can access your API and under what conditions.

Use the API endpoint’s DNS names to access the API from your VPC. For example, https://api-id.execute-api.region.amazonaws.com/stage.

https://docs.aws.amazon.com/autoscaling/ec2/userguide/create-asg-instance-type-requirements.html#use-attribute-based-instance-type-selection-prerequisites

This option allows the solutions architect to use session tags to pass additional information about the federated user, such as the development unit name, to AWS1. Session tags are key-value pairs that you can define in your identity provider (IdP) and pass in your SAML assertion1. By using a deny action and a StringNotEquals condition in the IAM policy, you can prevent developers from accessing or modifying EC2 instances that belong to a different development unit2. This way, you can enforce fine-grained access control and prevent accidental or malicious incidents.

Passing session tags in SAML assertions

Using tags for attribute-based access control

This explanation is based on AWS documentation and best practices but is paraphrased, not a literal extract.

The current CI/CD pipeline uses an IAM user with long-lived access keys stored in GitHub Actions. The new requirement is that pipelines must not use long-lived secret keys. Instead, the solution should provide short-lived credentials with minimal operational overhead.

GitHub Actions natively supports integration with cloud providers using OpenID Connect (OIDC). With OIDC, GitHub acts as an identity provider that can issue OIDC tokens to workflows. On the AWS side, IAM supports configuring an OIDC identity provider and roles that can be assumed by principals presenting valid OIDC tokens through the sts:AssumeRoleWithWebIdentity API. This pattern enables short-lived, automatically rotated credentials for CI/CD jobs without storing long-lived secrets.

In the correct solution (option B), you configure an IAM OIDC identity provider for GitHub in the AWS account. You then create a new IAM role with a trust policy that allows the Github OIDC provider to call sts:AssumeRoleWithWebIdentity, with conditions that restrict which repositories or workflows can assume the role. The existing IAM policy that grants deployment permissions is attached to that role. In GitHub Actions, you update the pipeline configuration to request an OIDC token and assume the IAM role at runtime. Each workflow run receives short-lived credentials without storing static keys, and AWS automatically handles the token verification and temporary credential issuance. This approach is the AWS-recommended pattern for integrating GitHub Actions with AWS without long-lived secrets and has low operational overhead once configured.

Option A uses SAML 2.0, which is typically used for enterprise single sign-on for users, not for GitHub Actions workflows. GitHub does not natively use SAML to obtain AWS credentials for CI/CD pipelines in the same streamlined way as OIDC, and implementing a SAML-based integration would add unnecessary complexity.

Option C introduces Amazon Cognito as an indirection layer. Although Cognito can federate with external identity providers, including social providers, using it as an intermediary to obtain temporary AWS credentials for a machine-to-machine CI/CD pipeline is not necessary when IAM OIDC federation with GitHub is directly supported. This adds additional configuration and operational overhead.

Option D uses IAM Roles Anywhere with client certificates from AWS Private CA. Roles Anywhere is designed for workloads running outside AWS that need to assume IAM roles using X.509 certificates instead of access keys. While technically possible, it requires managing private certificates, trust anchors, and a credential helper tool, which is more complex and operationally heavier than the direct OIDC integration specifically designed for GitHub Actions.

Therefore, configuring an IAM OIDC identity provider for GitHub and creating an IAM role to be assumed via sts:AssumeRoleWithWebIdentity (option B) meets the requirement to replace long-lived secret keys with short-lived credentials with the least operational overhead.

Create a new customer-managed prefix list in the security team’s AWS account. Populate the customer-managed prefix list with all internal CIDR ranges. Share the customer-managed prefix list with the organization by using AWS Resource Access Manager. Notify the owner of each AWS account to allow the new customer-managed prefix list ID in their security groups. This solution meets the requirements with the least amount of operational overhead as it requires the security team to create and maintain a single customer-managed prefix list, and share it with the organization using AWS Resource Access Manager. The owners of each AWS account are then responsible for allowing the prefix list in their security groups, whicheliminates the need for the security team to manually notify each account owner when changes are made. This solution also eliminates the need for a separate AWS Lambda function in each account, reducing the overall complexity of the solution.

https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/lambda-edge-how-it-works-tutorial.html

https://docs.aws.amazon.com/waf/latest/developerguide/marketplace-managed-rule-groups.html

This option allows the company to use AWS Client VPN to enable secure and private access to the Amazon ES cluster from any location1. By configuring and setting up an AWS Client VPN endpoint, the company can create a secure tunnel between the developers’ devices and the VPC2. By associating the Client VPN endpoint with a subnet in the VPC, the company can ensure that the trafficfrom the developers’ devices is routed to the Amazon ES cluster within the VPC3. By configuring a Client VPN self-service portal, the company can enable the developers to download and install the client for Client VPN, which is based on OpenVPN4. By instructing the developers to connect by using the client for Client VPN, the company can allow them to access Amazon ES to analyze and visualize logs directly from their local development machines.

What is AWS Client VPN?

Creating a Client VPN endpoint

Associating a target network with a Client VPN endpoint

Configuring a self-service portal

https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/DownloadDistS3AndCustomOrigins.html

https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/high_availability_origin_failover.html

You can set up CloudFront with origin failover for scenarios that require high availability. To get started, you create an origin group with two origins: a primary and a secondary. If the primary origin is unavailable, or returns specific HTTP response status codes that indicate a failure, CloudFront automatically switches to the secondary origin.

The Auto Scaling group can automatically launch and terminate EC2 instances based on the demand and health of the web application. The launch template can specify the configuration of the EC2 instances, such as the AMI, instance type, security group, and user data. The existing ALB can distribute the traffic to the EC2 instances in the Auto Scaling group. The existing EC2 instances can be attached to the Auto Scaling group without deleting them or the ALB. This option minimizes downtime and preserves the current setup of the web application. References: [What is Amazon EC2 Auto Scaling?], [Launch templates], [Attach a load balancer to your Auto Scaling group], [Attach EC2 instances to your Auto Scaling group]

https://aws.amazon.com/premiumsupport/knowledge-center/aws-sftp-endpoint-type/

The best solution is to deploy two firewall appliances into the shared services VPC, each in a separate Availability Zone, and create a new Gateway Load Balancer to distribute traffic to them. A Gateway Load Balancer is designed for high performance and high availability scenarios with third-party network virtual appliances, such as firewalls. It operates at the network layer and maintains flow stickiness and symmetry to a specific appliance instance. It also uses the GENEVE protocol to encapsulate traffic between the load balancer and the appliances. To route traffic from other VPCs to the Gateway Load Balancer, a VPC Gateway Load Balancer endpoint is needed. This is a VPC endpoint that provides private connectivity between the appliances in the shared services VPC and the application servers in other VPCs. The endpoint must be added as the next hop in the route table for the shared services VPC. This solution ensures reliability and minimizes failover time between firewall appliances within a single AWS Region. References: What is a Gateway Load Balancer?, Gateway load balancer - Azure Load Balancer, Introducing Azure Gateway Load Balancer: Deploy and scale network …

This solution will meet the requirement with the least operational overhead because it directly denies the creation of the security group inbound rule with 0.0.0.0/0 as the source, which is the exact requirement. Additionally, it does not require any additional steps or resources such as invoking a Lambda function or adding a Config rule.

An SCP (Service Control Policy) is a policy that you can use to set fine-grained permissions for your AWS accounts within your organization. You can use SCPs to set permissions for the root user of an account and to delegate permissions to IAM users and roles in the accounts. You can use SCPs to set permissions that allow or deny access to specific services, actions, and resources.

To implement this solution, you would need to create an SCP that denies the ec2:AuthorizeSecurityGroupIngress action when the value of the aws:SourceIp condition key is 0.0.0.0/0. This SCP would then be applied to the NonProd OU. This would ensure that any security group inbound rule that includes 0.0.0.0/0 as the source will be denied, thus meeting the requirement.

Comprehensive and Detailed Explanation From Exact Extract:

D is correct because the requirement explicitly calls for (1) dedicated private connectivity, (2) automated and centrally managed operation, and (3) connectivity between on-premises data centers and multiple AWS Regions.

AWS Direct Connect provides dedicated private connectivity from on-premises environments to AWS. This is the key differentiator versus internet-based connectivity options when performance and consistency are required to meet strict SLAs.

AWS Cloud WAN provides centralized, policy-based management of a global network across Regions and on-premises attachments. Using Direct Connect attachments to Cloud WAN lets the company centrally define routing/segmentation policies and connect multiple data centers to multiple Regions in a consistent, automated way—well aligned to “automated and centrally managed” requirements for a multi-Region payment platform.

Why the other options are incorrect:

A (Global Accelerator) improves performance for internet-facing endpoints using Anycast and edge routing, but it does not provide private, dedicated connectivity between on-premises networks and AWS services. It is not the right tool for private HSM-to-AWS service connectivity.

B (Site-to-Site VPN + Transit Gateway) can be centrally routed with Transit Gateway, but VPN tunnels run over the public internet and are not “dedicated private connectivity.” They can be acceptable for many cases, but they do not best meet “dedicated private connectivity” and strict SLA expectations compared to Direct Connect.

C (CloudHSM) changes the architecture by moving tokenization into AWS-managed HSM infrastructure. The question states the company manages on-premises HSMs and needs private connectivity between those HSMs and AWS payment services, so replacing them with CloudHSM does not satisfy the stated connectivity requirement.

A is correct because:

CloudFormation templatesenable repeatable infrastructure deployment.

Route 53 latency-based routingensures users hit the closest Region.

DynamoDB global tablesallow multi-Region, active-active replication of application data.

Manual console work (B) is not scalable.

C lacks EC2/ALB replication.

D adds unnecessary services like Beanstalk and doesn’t scale cleanly.

This solution requires minimal operational overhead, as it only requires setting up AWS IoT Core and creating a thing for each device. (Reference: AWS Certified Solutions Architect - Professional Official Amazon Text Book, Page 537)

AWS IoT Core is a fully managed service that enables secure, bi-directional communication between internet-connected devices and the AWS Cloud. It supports the MQTT protocol and includes built-in device authentication and access control. By using AWS IoT Core, the company can easily provision and manage the X.509 certificates for each device, and connect the devices to the service with minimal operational overhead.

The application is business-critical with an SLA requirement of 99.95% uptime and is currently limited by an on-premises data center. The architecture includes a PostgreSQL database and separate business logic and presentation layers. Remote users experience high latency when accessing the application, which is latency-sensitive.

To meet or exceed a 99.95% SLA for the database while minimizing operational burden, a managed database service with built-in high availability is preferred. Amazon Aurora PostgreSQL-compatible editions provide high availability and durability by automatically replicating data across multiple Availability Zones in a Region. Aurora can offer higher availability and faster failover compared to self-managed databases on EC2, and offloads patching, backups, and maintenance.

For the application and presentation layers, running them on Amazon EC2 instances in an Auto Scaling group behind an Application Load Balancer preserves the basic architecture (stateless application servers behind a load balancer) while moving to a managed, highly available environment that can scale out and in automatically based on demand. This results in minimal changes to the application components and provides improved resilience compared to on-premises VMs.

Remote users experience slow load times because their desktop clients connect over higher-latency networks to the on-premises servers. By using Amazon AppStream 2.0, the application can be streamed from AWS to end users. AppStream 2.0 runs the desktop application close to the backend database and application servers within AWS, and streams only the display and input over the network. This significantly reduces the effect of latency between the user and the application backend, improving user experience, especially for latency-sensitive desktop applications, without requiring major changes to the application itself.

Option A uses a PostgreSQL database on EC2, which requires the customer to manage availability, backups, patching, and failover. This does not provide the same managed high-availability guarantees as Aurora or RDS Multi-AZ. It also suggests allocating a full Amazon WorkSpaces desktop per user, which can be more expensive and more complex than streaming only the application via AppStream 2.0.

Option C uses Amazon RDS for PostgreSQL with Multi-AZ configuration, which provides high availability and is a valid managed option. However, it runs the application and presentation layers on Fargate containers behind a Network Load Balancer. NLB is typically used for TCP-level load balancing and is better suited for protocols that require low-level handling, whereas HTTP/HTTPS web applications are commonly placed behind an Application Load Balancer, which provides advanced HTTP routing features. Also, ElastiCache can reduce database load and improve response times but does not directly solve the end-user network latency issue in a desktop client scenario.

Option D is incorrect because Amazon Redshift is a data warehouse service, not an operational PostgreSQL-compatible database for transaction processing. It is not appropriate for hosting the application’s primary relational database. CloudFront accelerates delivery of static and cached web content, not interactive desktop client-server traffic.

Therefore, migrating the database to Aurora PostgreSQL, running the application and presentation layers on EC2 Auto Scaling behind an Application Load Balancer, and using Amazon AppStream 2.0 to deliver the application to remote users (option B) meets the availability requirements, improves user experience, requires relatively little change to the application architecture, and minimizes operational costs compared to more complex or less-managed alternatives.

A is correct because AWS recommends using oneconnection alias per Region, associated witheach directory. Then, configure a Route 53 failover policy so that if the primary Region becomes unhealthy, users are directed to the failover Region automatically. “Evaluate Target Health” ensures automatic detection and failover.

The CAPABILITY_NAMED_IAM capability is required when creating or updating CloudFormation stacks that contain IAM resources with custom names. This capability acknowledges that the template mightcreate IAM resources that have broad permissions or affect other resources in the AWS account. The stack set’s template contains an IAM role that has a custom name, so this capability is needed. Enabling the new Regions in all relevant accounts is also necessary to deploy the stack set across multiple Regions and accounts.

Option B is incorrect because the Service Quotas console is used to view and manage the quotas for AWS services, not for CloudFormation stacks. The number of stacks per Region per account is not a service quota that can be increased.

Option C is incorrect because the SELF_MANAGED permissions model is used when the administrator wants to retain full permissions to manage stack sets and stack instances. This model does not affect the creation of the stack set or the requirement for the CAPABILITY_NAMED_IAM capability.

Option D is incorrect because an administration role ARN is optional when creating a stack set. It is used to specify a role that CloudFormation assumes to create stack instances in the target accounts. It does not affect the creation of the stack set or the requirement for the CAPABILITY_NAMED_IAM capability.

1: AWS CloudFormation stack sets

2: Acknowledging IAM resources in AWS CloudFormation templates

3: AWS CloudFormation stack set permissions

This explanation is based on AWS documentation and best practices but is paraphrased, not a literal extract.

The company wants to move from a manual EC2 and EBS-based workflow to a containerized application on Amazon EKS and automate data movement. The solution must:

Support automated transfer of raw and processed data.

Offer multiprotocol support.

Be directly usable from the EKS cluster as a mounted volume.

Minimize operational effort by using managed services where possible.

AWS DataSync is a managed service designed to move data between on-premises storage and AWS storage services or between AWS storage services. It can perform scheduled or continuous transfers with minimal operational overhead. For storage accessible from Amazon EKS, a shared file system that supports mounting as a volume is appropriate.

Amazon FSx for NetApp ONTAP provides a fully managed file system with multiprotocol support, including NFS and SMB, and supports features such as snapshots and storage efficiencies. Because it supports multiple protocols, it satisfies the requirement for multiprotocol access and can be mounted by applications running in Amazon EKS using standard Kubernetes persistent volume mechanisms.

In the correct solution (option C), DataSync is used to copy raw data from the on-premises environment to FSx for NetApp ONTAP. The FSx for NetApp ONTAP file system is then mounted as a volume in the EKS cluster, allowing the containerized analytics processing logic to read and write data directly. After processing, DataSync is again used to copy processed data from FSx for NetApp ONTAP to Amazon S3 for long-term storage. This leverages DataSync’s native integration with both FSx for NetApp ONTAP and Amazon S3, and avoids the need to run or manage custom upload tooling.

Option A uses Amazon EFS, which supports NFS but does not provide multiprotocol support (for example, SMB), so it does not fully meet the multiprotocol requirement. It also introduces AWS Transfer for SFTP for the processed data upload, which adds an additional managed endpoint and SFTP-based flow, increasing complexity relative to using DataSync end-to-end.

Option B uses Amazon FSx for Lustre, which is optimized for high-performance compute workloads and integrates well with S3, but it is not a multiprotocol file system and is typically accessed via NFS. It does not meet the stated multiprotocol requirement.

Option D uses FSx for NetApp ONTAP (which supports multiprotocol) but relies on AWS Transfer for SFTP to move processed data to S3. While this can work, it adds another managed input endpoint and requires SFTP client configuration and management. Using DataSync directly from FSx for NetApp ONTAP to Amazon S3 (as in option C) is more straightforward, better suited for automated large-scale transfers, and involves less operational overhead.

Therefore, option C meets all the requirements with the least operational effort by using DataSync with FSx for NetApp ONTAP and S3.

This option allows the solutions architect to use a private hosted zone to host DNS records that are only accessible within the VPC1. By associating the private hosted zone with the VPC, the solutions architect can ensure that DNS queries from the VPC are routed to the private hosted zone2. By activating the enableDnsSupport attribute and the enableDnsHostnames attribute for the VPC, the solutions architect can enable DNS resolution and hostname assignment for instances in the VPC3. By creating a new VPC DHCP options set, and configuring domain-name-servers=AmazonProvidedDNS, the solutions architect can use Amazon-provided DNS servers to resolve DNS queries from instances in the VPC4. By associating the new DHCP options set with the VPC, the solutions architect can apply the DNS settings to all instances in the VPC5.

What is Amazon Route 53 Resolver?

Associating a private hosted zone with your VPC

Using DNS with your VPC

DHCP options sets

Modifying your DHCP options

The AWS Application Discovery Service can help plan migration projects by collecting data about on-premises servers, such as configuration, performance, and network connections. The data collection agent is a lightweight software that can be installed on each server to gather this information. This option is more cost-effective than agentless discovery, which requires deploying a virtual appliance in the VMware environment, or using CloudWatch agent, which incurs additional charges for CloudWatch Logs. Scanning the servers over a VPN is not a valid option for AWS Application Discovery Service. References: What is AWS Application Discovery Service?, Data collection methods

https://aws.amazon.com/premiumsupport/knowledge-center/private-hosted-zone-different-account/

However A's explanation is incorrect -https://docs.aws.amazon.com/organizations/latest/userguide/orgs_manage_policies_scps.html

"SCPs are similar to AWS Identity and Access Management (IAM) permission policies and use almost the same syntax. However, an SCP never grants permissions."

SCPs alone are not sufficient to granting permissions to the accounts in your organization. No permissions are granted by an SCP. An SCP defines a guardrail, or sets limits, on the actions that the account's administrator can delegate to the IAM users and roles in the affected accounts. The administrator must still attach identity-based or resource-based policies to IAM users or roles, or to the resources in your accounts to actually grant permissions. The effective permissions are the logical intersection between what is allowed by the SCP and what is allowed by the IAM and resource-based policies.

The company should use infrastructure as code (IaC) to provision the new infrastructure in the DR Region. The company should create a cross-Region read replica for the DB instance. The company should set up AWS Elastic Disaster Recovery to continuously replicate the EC2 instances to the DR Region. The company should run the EC2 instances at the minimum capacity in the DR Region. The company should use an Amazon Route 53 failover routing policy to automatically fail over to the DR Region in the event of a disaster. The company should increase the desired capacity of the Auto Scaling group. This solution will meet the requirements most cost-effectively because AWS Elastic Disaster Recovery (AWS DRS) is a service that minimizes downtime and data loss with fast, reliable recovery of on-premises and cloud-based applications using affordable storage, minimal compute, and point-in-time recovery. AWS DRS enables RPOs of seconds and RTOs of minutes1. AWS DRS continuously replicates data from the source servers to a staging area subnet in the DR Region, where it uses low-cost storage and minimal compute resources to maintain ongoing replication. In the event of a disaster, AWS DRS automatically converts the servers to boot and run natively on AWS and launches recovery instances on AWS within minutes2. By using AWS DRS, the company can save costs by removing idle recovery site resources and paying for the full disaster recovery site only when needed. By creating a cross-Region read replica for the DB instance, the company can have a standby copy of its primary database in a different AWS Region3. By using infrastructure as code (IaC), the company can provision the new infrastructure in the DR Region in an automated and consistent way4. By using an Amazon Route 53 failover routing policy, the company can route traffic to a resource that is healthy or to another resource when the first resource becomes unavailable.

The other options are not correct because:

Using AWS Backup to create cross-Region backups for the EC2 instances and the DB instance would not meet the RPO and RTO requirements. AWS Backup is a service that enables you to centralize and automate data protection across AWS services. You can use AWS Backup to back up your application data across AWS services in your account and across accounts. However, AWS Backup does not provide continuous replication or fast recovery; it creates backups at scheduled intervalsand requires manual restoration. Creating backups every 30 seconds would also incur high costs and network bandwidth.

Creating an Amazon API Gateway Data API service integration with Amazon Redshift would not help with disaster recovery. The Data API is a feature that enables you to query your Amazon Redshift cluster using HTTP requests, without needing a persistent connection or a SQL client. It is useful for building applications that interact with Amazon Redshift, but not for replicating or recovering data.

Creating an AWS Data Exchange datashare by connecting AWS Data Exchange to the Redshift cluster would not help with disaster recovery. AWS Data Exchange is a service that makes it easy for AWS customers to exchange data in the cloud. You can use AWS Data Exchange to subscribe to a diverse selection of third-party data products or offer your own data products to other AWS customers. A datashare is a feature that enables you to share live and secure access to your Amazon Redshift data across your accounts or with third parties without copying or moving the underlying data. It is useful for sharing query results and views with other users, but not for replicating or recovering data.

Create a Transit Gateway:

Step 1: In the AWS Management Console, navigate to the VPC Dashboard.

Step 2: Select "Transit Gateways" and click on "Create Transit Gateway".

Step 3: Configure the transit gateway by providing a name and setting the options for Amazon side ASN and VPN ECMP support as needed.

Step 4: Attach each of the company’s VPCs and relevant subnets to the transit gateway. This centralizes the network management and simplifies the routing configurations, supporting scalable and flexible network architecture.

Set Up AWS PrivateLink:

Step 1: Create a Network Load Balancer (NLB) in the management VPC that points to the compute resource responsible for license validation.

Step 2: Create an AWS PrivateLink endpoint service pointing to this NLB.

Step 3: Allow each customer's VPC to create an interface endpoint to this PrivateLink service. This setup enables secure and private communication between the customer VPCs and the management VPC, ensuring one-way access from each customer's VPC to the management VPC for license validation.

This combination leverages the benefits of AWS Transit Gateway for scalable and centralized routing, and AWS PrivateLink for secure and private service access, meeting the requirement with minimal operational overhead.

References

Amazon VPC-to-Amazon VPC Connectivity Options

AWS PrivateLink - Building a Scalable and Secure Multi-VPC AWS Network Infrastructure

Connecting Your VPC to Other VPCs and Networks Using a Transit Gateway

This option allows the solutions architect to use an identity policy that grants the user readand write access to their own personal folder on the S3 bucket1. By adding a condition that specifies that the S3 paths must be prefixed with ${aws:username}, the solutions architect can ensure that each scientist can only access their own folder2. By applying the policy on the scientists’ IAM user group, the solutions architect can simplify the management of permissions for all the scientists3. By configuring a trail with AWS CloudTrail to capture all object-level events in the S3 bucket, the solutions architect can record and store information about every action performed on the S3 objects4. By storing the trail output in another S3 bucket, thesolutions architect can secure and archive the log files5. By using Amazon Athena to query the logs and generate reports, the solutions architect can use a serverless interactive query service that can analyze data in S3 using standard SQL.

Identity-based policies

Policy variables

IAM groups

Object-level logging

Creating a trail that applies to all regions

[What is Amazon Athena?]

Transit GW + Direct Connect GW + Transit VIF + enabled SiteLink if two different DX locationshttps://aws.amazon.com/blogs/networking-and-content-delivery/introducing-aws-direct-connect-sitelink/

Comprehensive and Detailed in Depth Explanation:

C is correct because Amazon EFS offers a scalable, elastic file system that can be mounted on both on-premises servers (via AWS Direct Connect or VPN) and EC2 instances. This allows real-time access to shared content with no migration downtime.

D is correct because AWS Snowball Edge Storage Optimized devices are designed for transferring petabyte-scale data (up to 80 TB per device). It’s a best practice for moving 200 TB of archival data when speed and bandwidth are constraints.

GitHub Webhooks to Trigger CodePipeline:

Configure GitHub webhooks to trigger the AWS CodePipeline pipeline. This ensures that every code push to the repository automatically triggers the pipeline, initiating the build and deployment process.

Unit Testing with Jenkins and AWS CodeBuild:

Use Jenkins integrated with the AWS CodeBuild plugin to perform unit testing. Jenkins will manage the build process, and the results will be handled by CodeBuild.

Notifications for Bad Builds:

Configure Amazon SNS (Simple Notification Service) to send alerts for any failed builds. This keeps the engineering team informed of build issues immediately, allowing for quick resolutions.

Blue/Green Deployment with AWS CodeDeploy:

Utilize AWS CodeDeploy with a blue/green deployment strategy. This method reduces downtime and risk by running two identical production environments (blue and green). CodeDeploy shifts traffic between these environments, allowing you to test in the new environment (green) while the old environment (blue) remains live. If issues arise, you can quickly roll back to the previous environment.

This solution provides seamless, zero-downtime deployments, and the ability to quickly roll back changes if necessary, fulfilling the requirements of the startup company.

References

AWS DevOps Blog on Integrating Jenkins with AWS CodeBuild and CodeDeploy【32】.

Plain English Guide to AWS CodePipeline with GitHub【33】.

Jenkins Plugin for AWS CodePipeline【34】.

Configuring read replicas for Amazon RDS MySQL and using the single reader endpoint in the web application can significantly reduce the load on the backend database tier, improving overall application performance. Additionally, implementing an Amazon ElastiCache cluster between the web application and RDS MySQL instances can further reduce database load by caching frequently accessed data, thereby enhancing the application's resilience and scalability. These changes address the root cause of the outage by alleviating the database tier's high load and preventing similar issues in the future.

AWS Documentation on Amazon RDS Read Replicas and Amazon ElastiCache provides comprehensive guidance on improving application performance and scalability by offloading read traffic from the primary database and caching common queries. These solutions are in line with AWS best practices for building resilient and scalable web applications.

https://docs.aws.amazon.com/elasticbeanstalk/latest/dg/AWSHowTo.cloudwatch.html Elastic Beanstalk automatically uses Amazon CloudWatch to help you monitor your application and environment status. You can navigate to the Amazon CloudWatch console to see your dashboard and get an overview of all of your resources as well as your alarms. You can also choose to view more metrics or add custom metrics.

The modernized application needs to store and serve video files up to 4 GB. Large binary content should not be stored directly in a database because it negatively affects database performance, scaling, and backup/restore times. The standard AWS pattern is to store large objects in Amazon S3 and keep only references (such as keys or URLs) in the database.

User profile data is simple, text-based, and currently lives in a single table. This is a good fit for a key-value or document-style data model such as Amazon DynamoDB, which provides fully managed horizontal scaling and predictable performance at scale.

Option B migrates the profile table to DynamoDB using AWS DMS with AWS Schema Conversion Tool (SCT). Video files are stored as objects in Amazon S3, which is designed for large, durable, and highly scalable object storage. The DynamoDB item stores only the S3 key that corresponds to each user’s video, keeping the database small and responsive. S3 can scale rapidly and independently of the database, and offloads the heavy I/O load of large objects away from the transactional store, so overall application performance is not negatively impacted by video storage or retrieval.

Option A stores the videos as base64-encoded strings in a TEXT column in a relational database. Storing multi-gigabyte objects directly in a relational database table leads to large row sizes, larger indexes, longer backup times, and degraded performance as the table grows. This does not meet the requirement to avoid affecting application performance.

Option C uses Amazon Keyspaces (for Apache Cassandra) as the profile store and S3 for video. While storing videos in S3 is correct, Keyspaces is typically chosen when there is already a Cassandra-based workload or a need for Cassandra-compatible interfaces. For a simple single-table user profile store being migrated from MySQL, DynamoDB is the more natural, managed choice and aligns better with AWS migration and modernization guidance.

Option D stores the videos as base64-encoded strings in DynamoDB items. DynamoDB has a maximum item size of 400 KB, which is far smaller than the 4 GB video size requirement. This makes option D technically infeasible.

Therefore, migrating user profile data to DynamoDB and storing video files in Amazon S3, with S3 keys stored in the corresponding DynamoDB items (option B), provides scalable video storage and protects application performance.

https://docs.aws.amazon.com/autoscaling/ec2/userguide/as-suspend-resume-processes.html

To ensure that game assets are fetched from the closest region and have a fallback option in case the assets become unavailable in the closest region, a solution architect should leverage Amazon CloudFront, a global content delivery network (CDN) service. By creating an Amazon CloudFront distribution and setting up origin groups, the architect can specify multiple origins (in this case, the Application Load Balancers in each region). The primary origin will serve content under normal circumstances, and if the content becomes unavailable, CloudFront will automatically switch to the secondary origin. This approach not only meets the requirement of regional proximity and redundancy but also optimizes latency and enhances the gaming experience by serving assets from the nearest geographical location to the end-user.

AWS Documentation on Amazon CloudFront and origin groups provides detailed instructions on setting up distributions with multiple origins for high availability and performance optimization. Additionally, AWS whitepapers and best practices on content delivery and global applications offer insights into effectively utilizing CloudFront and other AWS services to achieve low latency and high availability.

This solution allows for deploying the Lambda function and API Gateway endpoint to another region, providing a failover option in case of any issues in the primary region. Using Route 53's failover routing policy allows for automatic routing of traffic to the healthy endpoint, ensuring that the application is available even in case of issues in one region. This solution provides a cost-effective and simple way to implement failover while minimizing operational overhead.

https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/manage-cost-categories.html

https://aws.amazon.com/premiumsupport/knowledge-center/cost-explorer-analyze-spending-and-usage/

https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/manage-cost-categories.htmlhttps://docs.aws.amazon.com/cost-management/latest/userguide/ce-enable.html

The best combination of actions to meet the company’s requirements is Options A, C, and F.

Option A involves activating the user-defined cost allocation tags that represent the application and the team. This will allow the company to assign costs to different applications or teams, and will allow them to be tracked in the monthly AWS bill.

Option C involves creating a cost category for each application in Billing and Cost Management. This will allow the company to easily identify and compare costs across different applications and teams.

Option F involves enabling Cost Explorer. This will allow the company to view the costs of their AWS resources over the last 12 months and to create forecasts for the next 12 months.

These recommendations are in line with the official Amazon Textbook and Resources for the AWS Certified Solutions Architect - Professional certification. In particular, the book states that “You can use cost allocation tags to group your costs by application, team, or other categories” (Source:https://d1.awsstatic.com/training-and-certification/docs-sa-pro/AWS_Certified_Solutions_Architect_Professional_Exam_Guide_EN_v1.5.pdf).Additionally , the book states that “Cost Explorer enables you to view the costs of your AWS resources over the last 12 months and to create forecasts for the next 12 months” (Source:https://d1.awsstatic.com/training-and-certification/docs-sa-pro/AWS_Certified_Solutions_Architect_Professional_Exam_Guide_EN_v1.5.pdf).

This explanation is based on AWS documentation and best practices but is paraphrased, not a literal extract.

The scenario describes a central event broker and multiple microservices running in separate AWS accounts that all need to consume the same events. The requirement is to distribute events from a central location to many microservices across accounts in a scalable and loosely coupled way, as part of modernizing to a microservices architecture.

Amazon EventBridge is a serverless event bus service designed for event-driven architectures. It supports centralized event buses, rich content-based filtering with rules, and cross-account event routing. With EventBridge, you can create an event bus in a central account and define rules that match specific event patterns (for example, by microservice or event type). Each rule can have one or more targets, including event buses in other AWS accounts. This supports the pattern of having a central event bus in one account and distributing relevant events to other accounts, where each microservice consumes events either directly from its own event bus or through additional rules and targets in its own account.

In this solution, you create a new EventBridge event bus in the central account and grant the appropriate permissions for cross-account access (option B). You then define EventBridge rules on the central event bus, filtered per microservice or per event category, and configure the rules to send events to the respective event buses or targets in the microservices’ accounts. EventBridge handles the fan-out and delivery of events across accounts in a managed, scalable way, which aligns with the modernization goal and reduces the operational overhead of managing custom routing or polling logic.

Option A uses an SNS topic with SQS queues in each account. This is a valid fan-out pattern and supports cross-account subscriptions, but it is more suited to traditional pub/sub messaging and does not provide the event routing, filtering, and observability features that EventBridge offers for modern event-driven microservices. In scenarios that explicitly mention an event broker and modernization, EventBridge is the recommended service.

Option C is incorrect because Kinesis Data Streams is designed for high-throughput streaming data and requires building and managing consumer applications. The description in the option is also technically inaccurate; Kinesis does not “invoke” microservices directly as event targets in the same way as EventBridge or SNS does. Instead, applications must read from the stream.

Option D uses a single central SQS queue that all microservices read from. SQS provides at-least-once delivery to competing consumers, which means multiple consumers reading from the same queue will typically share messages rather than each getting all messages. This does not satisfy the requirement for multiple microservices to each receive the same events independently. It also reduces decoupling and observability compared to an event bus model.

Therefore, creating an Amazon EventBridge event bus in the central account with rules to distribute events across accounts (option B) best meets the requirements for distributing events from a central broker to multiple microservices across accounts in a modernized architecture.

This solution meets the requirements by using Application Auto Scaling to automatically increase capacity during the peak period, which will handle the double the average load. And by purchasing reserved RCUs and WCUs to match the average load, it will minimize the cost of the table for the rest of the week when the load is close to the average.

Configuring AWS CloudTrail to log S3 data events will enable logging all activities for objects in the S3 bucket1. Data events are object-level API operations such as GetObject, DeleteObject, and PutObject1. Configuring Amazon S3 to send object deletion events to an Amazon EventBridge event bus that publishes to an Amazon Simple Notification Service (Amazon SNS) topic will enable sending email notifications every time there is an attempt to delete data in the S3 bucket2. EventBridge can route events from S3 to SNS, which can send emails to subscribers2. Configuring a new S3 bucket to store the logs with an S3 Lifecycle policy will enable keeping the logs for 5 years in a cost-effective way3. A lifecycle policy can transition the logs to a cheaper storage class such as Glacier or delete them after a specified period of time3.

https://aws.amazon.com/premiumsupport/knowledge-center/s3-upload-large-files/

Enabling S3 Transfer Acceleration on the S3 bucket and configuring the app to use the Transfer Acceleration endpoint for uploads will improve the app's performance for these uploads by leveraging Amazon CloudFront's globally distributed edge locations to accelerate the uploads. Breaking the video files into chunks and using a multipart upload to transfer files to Amazon S3 will also improve the app's performance by allowing parts of the file to be uploaded in parallel, reducing the overall upload time.

This option allows the solutions architect to use a DeletionPolicy attribute to specify how AWS CloudFormation handles the deletion of an S3 bucket when the stack is deleted1. By setting the value of Delete, the solutions architect can instruct CloudFormation to delete the bucket and all of itscontents1. This option does not require any major changes to the application’s architecture or any additional resources.

Deletion policies

https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/placement-groups.html#placement- groups-cluster

minimizes operational + microservices that run on containers = AWS Elastic Beanstalk

To enhance the network connectivity solution's availability, fault tolerance, and security in a cost-effective manner, adding a dynamic private IP AWS Site-to-Site VPN as a secondary path is a viable option. This VPN serves as a resilient backup for the Direct Connect connection, ensuring continuous data flow even if the primary path fails. Implementing MACsec (Media Access Control Security) on the Direct Connect connection further secures the data in transit by providing encryption, thus addressing the security requirement. This solution strikes a balance between cost and operational efficiency, avoiding the higher expenses associated with provisioning an additional Direct Connect connection.

AWS Documentation on AWS Direct Connect and AWS Site-to-Site VPN provides insights into setting up resilient and secure network connections. Additionally, information on MACsec offers guidance on how to implement encryption for Direct Connect connections, aligning with best practices for secure and highly available network architectures.

 This solution will provide the highest availability for the database, as the read replicas will allow the database to be available in multiple Regions, thus reducing the chances of disruption. Additionally, the promotion of the cross-Region read replica to become a standalone DB instance will ensure that the database is still available even if one of the Regions experiences disruptions.

https://aws.amazon.com/rds/aurora/global-database/

Option C is correct because using Auto Scaling groups for the backend services allows the company to scale up or down the number of EC2 instances based on the demand and traffic. This way, the backend services can handle more requests during peak seasons without compromising performance or availability. Using DynamoDB auto scaling allows the company to adjust the provisioned read and write capacity of the table or index automatically based on the actual traffic patterns. This way, the table or index can handle sudden increases or decreases in workload without throttling or overprovisioning1.

Option A is incorrect because migrating the backend services to AWS Lambda may require significant development effort to rewrite the code and test the functionality. Moreover, increasing the read and write capacity of DynamoDB manually may not be efficient or cost-effective, as it does not account for the variability of the workload. The company may end up paying for unused capacity or experiencing throttling if the workload exceeds the provisioned capacity1.

Option B is incorrect because migrating the backend services to AWS Lambda may require significant development effort to rewrite the code and test the functionality. Moreover, configuring DynamoDB to use global tables may not be necessary or beneficial for the company, as global tables are mainly used for replicating data across multiple AWS Regions for fast local access and disaster recovery. Global tables do not automatically scale the provisioned capacity of each replica table; they still require manual or auto scaling settings2.

Option D is incorrect because using Amazon Simple Queue Service (Amazon SQS) and an AWS Lambda function to write to DynamoDB may introduce additional complexity and latency to the application architecture. Amazon SQS is a message queue service that decouples and coordinates the components of a distributed system. AWS Lambda is a serverless compute service that runs code in response to events. Using these services may require significant development effort to integrate them with the backend services and DynamoDB. Moreover, they may not improve the read performance of DynamoDB, which may also be affected by high traffic3.

Auto Scaling groups

DynamoDB auto scaling

AWS Lambda

DynamoDB global tables

AWS Lambda vs EC2: Comparison of AWS Compute Resources - Simform

Managing throughput capacity automatically with DynamoDB auto scaling - Amazon DynamoDB

AWS Aurora Global Database vs. DynamoDB Global Tables

Amazon Simple Queue Service (SQS)

https://docs.aws.amazon.com/application-discovery/latest/userguide/discovery-agent.htmlhttps://docs.aws.amazon.com/migrationhub/latest/ug/ec2-recommendations.html

https://docs.aws.amazon.com/config/latest/developerguide/config-rule-multi-account-deployment.html

https://docs.aws.amazon.com/config/latest/developerguide/aggregate-data.html

https://docs.aws.amazon.com/organizations/latest/userguide/orgs_manage_policies_scps_examples_tagging.html

The company requires two different types of security analysis: vulnerability scanning for CVEs and code-level scanning for sensitive data exposure. Amazon Inspector provides both Lambda Standard Scanning and Lambda Code Scanning. These capabilities allow Inspector to examine Lambda deployment packages and Lambda layers for software vulnerabilities, and also perform static code analysis to detect insecure coding patterns. To meet the requirement that only certain Lambda functions receive deeper code scanning, Inspector allows enabling or disabling code scanning at the individual Lambda function level through the function's Monitor settings. Inspector additionally supports resource exclusion based on tagging, allowing administrators to prevent specific Lambda functions from participating in code scans. Tag-based exclusion satisfies the requirement to scan only a subset of Lambda functions.

Option B is required because enabling both Lambda standard scanning and Lambda code scanning activates the functionality necessary for CVE detection and code security analysis.

Option D is required because enabling scanning at the function level ensures that code scanning runs only for selected Lambda functions.

Option E is required because resource exclusion using tags allows the administrator to exclude non-target Lambda functions from code scanning automatically.

Options A and C do not satisfy the requirement because simply activating Amazon Inspector does not configure per-function scanning behavior, and GuardDuty Lambda Protection does not scan for CVEs or code vulnerabilities. Option F is not valid because Amazon Inspector does not perform scans directly against objects stored in Amazon S3; scanning occurs against the deployed Lambda function versions themselves.

The best option is to use Amazon SQS and AWS Lambda to create a serverless order processing application. Amazon SQS is a fully managed message queue service that can decouple the order receiving and processing components, making the application more scalable and fault-tolerant. AWS Lambda is a serverless compute service that can automatically scale to handle the incoming messages from the SQS queue and process them according to the business logic. AWS Lambda can also integrate with Amazon DynamoDB to store the processed orders in a fast and flexible NoSQL database. This approach eliminates the need to provision, manage, or scale any servers or containers, and reduces the operational overhead and cost.

Option A is not reliable because using an EC2-hosted database to receive the orders introduces a single point of failure and a scalability bottleneck. EC2 instances also require more management and configuration than serverless services.

Option C is not reliable because using AWS Step Functions to receive the orders adds unnecessary complexity and cost to the application. AWS Step Functions is a service that coordinates multiple AWS services into a serverless workflow, but it is not designed to handle high-volume, sporadic, or unpredictable traffic patterns. AWS Step Functions also charges per state transition, which can be expensive for a large number of orders. Launching an ECS container to process each order also requires more resources and management than invoking a Lambda function.

Option D is not reliable because using Amazon Kinesis Data Streams to receive the orders is not suitable for this use case. Amazon Kinesis Data Streams is a service that enables real-time processing of streaming data at scale, but it is not meant for asynchronous message queuing. Amazon Kinesis Data Streams requires consumers to poll the data from the stream, which can introduce latency and complexity. Amazon Kinesis Data Streams also charges per shard hour, which can be expensive for a sporadic traffic pattern.

Amazon SQS

AWS Lambda

Amazon DynamoDB

AWS Step Functions

Amazon ECS

Comprehensive and Detailed Explanation From Exact Extract:

B is correct because the requirement is to allocate shared EC2 infrastructure costs (ECS on EC2, shared cluster) down to the business groups with the least operational effort. With shared compute, the EC2 line-item charges are not naturally per “task,” so you need a cost allocation feature that can split shared costs using workload metadata. Split cost allocation data combined with the AWS Cost and Usage Report (CUR) and resource tags provides the standard AWS mechanism to distribute (“split”) shared resource costs across consumers for chargeback/showback reporting. This avoids operationally heavy changes (like redesigning clusters) and supports automated reporting.

Why the other options are incorrect:

A: Tagging the Auto Scaling group can only attribute costs at the ASG level, not split costs between multiple business groups sharing the same cluster capacity. It does not solve per-business-group allocation when capacity is shared.

C: Separate clusters per group would work but is higher operational overhead (more clusters to manage, scaling policies, capacity planning, governance), which violates the “least operational overhead” requirement.

D: Cost Categories are useful for mapping and organizing costs, but by themselves they do not solve the underlying need to split shared EC2 costs among multiple consumers unless you already have an accurate split basis. Option B directly targets split allocation of shared costs with tagging and CUR.

Comprehensive and Detailed Explanation From Exact Extract:

B is correct because Amazon FSx for Lustre provides a high-performance, POSIX-compliant file system that can be directly linked with Amazon S3 using data repository integration. This allows the EC2-based processing engine (which requires POSIX file operations) to read/write files through a mounted Lustre file system while automatically importing objects from S3 and exporting results back to S3 using Lustre’s S3 integration policies. This satisfies all requirements: POSIX file access, no need to change the application to use the S3 API, and automated synchronization for both inputs and outputs.

Why the other options are incorrect:

A: DataSync is designed for scheduled or task-based data transfers. It does not provide a mounted POSIX file system interface for the application to read/write “in place,” and the “without an agent” requirement is not a fit for an EC2-hosted POSIX workflow that needs continuous file semantics.

C: Amazon EFS is POSIX and mountable, but “data repository associations to S3” is not an EFS capability in the way described. EFS does not provide native bidirectional automatic sync with S3 using import/export policies like FSx for Lustre’s S3 integration.

D: S3 File Gateway provides a file interface backed by S3, but keeping cache coherent with explicit RefreshCache calls is operationally complex and not truly “automatic synchronization” in the sense required. It also introduces caching behaviors that can complicate immediate availability expectations.

RDS Proxyoptimizes and pools DB connections. By combining this withPrivateLink + NLB, other accounts can securely access the proxy endpoint without going over the internet or setting up peering for every new account.

RDS Proxy Docs

A is correct becauseSSM Agent must be installed and actively runningon the instance for it to be managed by Systems Manager. Imported VMs typically do not have the agent installed by default.

B is also correct becausean IAM instance profilewith at least the AmazonSSMManagedInstanceCore policy must be attached to the instance to allow communication with SSM.

Option C is unnecessary if the instance has internet access (via public IP).

Option D relates to migration assessments, not Systems Manager.

Option E is good practice but not required for the instance to appear as managed.

AWS Elastic Disaster Recovery (AWS DRS) is a service that minimizes downtime and data loss with fast, reliable recovery of on-premises and cloud-based applications using affordable storage, minimal compute, and point-in-time recovery1. Users can set up AWS DRS on their source servers to initiate secure data replication to a staging area subnet in their AWS account, in the AWS Region they select. Users can then launch recovery instances on AWS within minutes, using the most up-to-date server state or a previous point in time.

To configure a cloud backup of the application with AWS DRS, users need to create a VPC that has at least two public subnets, a virtual private gateway, and an internet gateway. AVPC is a logically isolated section of the AWS Cloud where users can launch AWS resources in a virtual network that they define2. A public subnet is a subnet that has a route to an internet gateway3. A virtualprivate gateway is the VPN concentrator on the Amazon side of the Site-to-Site VPN connection4. An internet gateway is a horizontally scaled, redundant, and highly available VPC component that allows communication between instances in the VPC and the internet. Users need to create at least two public subnets for redundancy and high availability. Users need to create a virtual private gateway and attach it to the VPC to enable VPN connectivity between the on-premises network and the target AWS network. Users need to create an internet gateway and attach it to the VPC to enable internet access for the replication servers.

To ensure that replication traffic does not travel through the public internet, users need to create an AWS Direct Connect connection and a Direct Connect gateway between the on-premises network and the target AWS network. AWS Direct Connect is a service that establishes a dedicated network connection from an on-premises network to one or more VPCs. A Direct Connect gateway is a globally available resource that allows users to connect multiple VPCs across different Regions to their on-premises networks using one or more Direct Connect connections. Users need to create an AWS Direct Connect connection between their on-premises network and an AWS Region. Users need to create a Direct Connect gateway and associate it with their VPC and their Direct Connect connection.

To ensure that the application is not accessible from the internet, users need to select the option to use private IP addresses for data replication during configuration of the replication servers. This option configures the replication servers with private IP addresses only, without assigning any public IP addresses or Elastic IP addresses. This way, the replication servers can only communicate with other resources within the VPC or through VPN connections.

Option A is incorrect because creating a VPC that has at least two private subnets, two NAT gateways, and a virtual private gateway is not necessary or cost-effective. A private subnet is a subnet that does not have a route to an internet gateway3. A NAT gateway is a highly available, managed Network Address Translation (NAT) service that enables instances in a private subnet to connect to the internet or other AWS services, but prevents the internet from initiating connections with those instances. Users do not need to createprivate subnets or NAT gateways for this use case, as they can use public subnets with private IP addresses for data replication.

Option C is incorrect because creating an AWS Site-to-Site VPN connection between the on-premises network and the target AWS network will not ensure that replication traffic does not travel through the public internet. A Site-to-Site VPN connection consists of two VPN tunnels between an on-premises customer gateway device and a virtual private gateway in your VPC4. The VPN tunnels are encrypted using IPSec protocols, but they still use public IP addresses for communication. Users need to use AWS Direct Connect instead of Site-to-Site VPN for this use case.

Option F is incorrect because selecting the option to ensure that the Recovery instance’s private IP address matches the source server’s private IP address during configuration of the launch settings for the target servers will not ensure that the application is not accessible from the internet. This option configures the Recovery instance with an identical private IP address as its source server when launched in drills or recovery mode. However, this option does not prevent assigning public IP addresses or Elastic IP addresses to the Recovery instance. Users need to select the option to use private IP addresses for data replication instead.

The best solution for this problem is to update the visibility timeout for the SQS queue to 3 hours. This is because when the visibility timeout is set to 1 hour, it means that if the EC2 instance doesn't process the message within an hour, it will be moved to the dead-letter queue. By increasing the visibility timeout to 3 hours, this should give the EC2 instance enough time to process the message before it gets moved to the dead-letter queue. Additionally, configuring scale-in protection for the EC2 instances during processing will help to ensure that the instances are not terminated while the messages are being processed.

https://aws.amazon.com/blogs/aws/new-attributes-based-access-control-with-aws-single-sign-on/

because Amazon CloudFront considers the case of parameter names and values when caching based on query string parameters , thus inconsistent query strings may cause CloudFront to forward mixed-cased/misordered requests to the origin. Triggering a Lambda@Edge function based on a viewer request event to sort parameters by name and force them to be lowercase is the best choice. https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/QueryStringParameters.html#query-string-parameters-optimizing-cachinghttps://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/lambda-cloudfront-trigger-events.html

https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/lambda-examples.html#lambda-examples-normalize-query-string-parameters

The best solution is to provision a new Amazon Elastic File System (Amazon EFS) file system that uses Max I/O performance mode and mount the EFS file system on each EC2 instance in the cluster. Amazon EFS is a fully managed, scalable, and elastic file storage service that supports the POSIX standard and can be accessed by multiple EC2 instances concurrently. Amazon EFS offers two performance modes: General Purpose and Max I/O. Max I/O mode is designed for highly parallelized workloads that can tolerate higher latencies than the General Purpose mode. Max I/O mode provides higher levels of aggregate throughput and operations per second, which are suitable for big data analytics applications. This solution meets all the requirements of the company. References: Amazon EFS Documentation, Amazon EFS performance modes

https://aws.amazon.com/solutions/implementations/git-to-s3-using-webhooks/

https://medium.com/mindorks/building-webhook-is-easy-using-aws-lambda-and-api-gateway-56f5e5c3a596

The company wants a centralized enforcement mechanism across hundreds of AWS accounts. The control must ensure that any S3 access points that product teams create are VPC-only and cannot be internet-accessible. The most operationally efficient solution is one that applies across the organization without requiring per-account deployments, per-bucket policies, or per-access-point configuration.

Service control policies (SCPs) in AWS Organizations are designed to provide centralized guardrails that define the maximum available permissions for accounts in an organization. By attaching an SCP at the organization root (or to OUs as needed), the company can enforce that no principal in any account can create an S3 access point unless the request specifies a VPC network origin. This aligns directly with the requirement to enforce “VPC-only” access points consistently across all accounts with minimal ongoing operational work.

Option B uses an SCP at the root to deny s3:CreateAccessPoint unless the s3:AccessPointNetworkOrigin condition key evaluates to VPC. This is the correct organization-wide preventive control.

Option A is not correct because an S3 access point resource policy is attached to an access point after it exists and is primarily used to control access to the access point. It is not a reliable organization-wide preventive mechanism to enforce how access points are created across hundreds of accounts. Also, it requires access point-by-access point management, which increases operational overhead.

Option C is less operationally efficient because StackSets deployment across hundreds of accounts introduces additional operational steps and drift management. It also relies on account-level IAM permissions being properly used and does not provide a simple, centralized “cannot be bypassed” guardrail in the same way that an SCP does.

Option D is incorrect because S3 bucket policies control access to bucket resources and objects. They do not function as an organization-wide control to prevent creation of access points across accounts, and they would require bucket-by-bucket management rather than a centralized enforcement mechanism.

Therefore, a root-level SCP that denies access point creation unless the access point network origin is VPC is the most operationally efficient enforcement approach.

Integrate ALB with OIDC IdP:

In the AWS Management Console, navigate to the Application Load Balancer (ALB) settings.

Configure the ALB to use the OpenID Connect (OIDC) IdP for authentication. This ensures that all requests routed through the ALB are authenticated using the IdP.

Set Up Authentication Rules:

Create a listener rule on the ALB that requires authentication. This rule will forward requests to the IdP for user authentication before allowing access to the backend services.

Restrict Unauthenticated Access:

Ensure the ALB only forwards requests to backend services if the user is authenticated. Unauthenticated requests should be blocked or redirected to the IdP for authentication.

Update CloudFront Configuration:

Modify the CloudFront distribution to forward authenticated requests to the ALB. Ensure that the ALB and API Gateway accept only requests coming through the CloudFront distribution to enforce consistent authentication and security.

By enforcing authentication at the ALB level, you ensure that all backend services are accessed only by authenticated users, enhancing the overall security of the web application

https://aws.amazon.com/solutions/implementations/automations-for-aws-firewall-manager/

In this solution, AWS Firewall Manager is used to manage AWS WAF rules across accounts in the organization. An AWS Systems Manager Parameter Store parameter is used to store account numbers and OUs to manage. This parameter can be updated as needed to add or remove accounts or OUs. An Amazon EventBridge rule is used to identify any changes to the parameter and to invoke an AWS Lambda function to update the security policy in the Firewall Manager administrative account. This solution allows for easy management of AWS WAF rules across multiple accounts with minimal operational overhead

They all mention using spot instances and EKS based on EC2. A spot instance is not appropriate for a production server and the company is developing new application designed for AWS Fargate, which means we must plan the future cost improvement including AWS Fargate. https://aws.amazon.com/savingsplans/compute-pricing/

The company should configure Amazon FSx for Lustre with an import and export policy. The company should link the new file system to an S3 bucket. The company should install the Lustre client and mount the document store to an Amazon EC2 instance by using NFS. This solution will meet the requirements with the least amount of effort because Amazon FSx for Lustre is a fully managed service that provides a high-performance filesystem optimized for fast processing of workloads such as machine learning, highperformance computing, video processing, financial modeling, and electronic design automation1. Amazon FSx for Lustre can be linked to an S3 bucket and can import data from and export data to the bucket2. The import and export policy can be configured to automatically import new or changed objects from S3 and export new or changed files to S33. This will ensure that the files are available to the public for download within 30 minutes. Amazon FSx for Lustre supports NFS version 3.0 protocol for Linux clients.

The other options are not correct because:

Migrating the application to an AWS Lambda function would require a lot of effort and may not be feasible for the existing server that generates many documents. Lambda functions have limitations on execution time, memory, disk space, and network bandwidth.

Setting up an Amazon S3 File Gateway would not work because S3 File Gateway does not support write-back caching, which means that files written to the file share are uploaded to S3 immediately and are not available locally until they are downloaded again. This would not provide fast local access to the files that the server generates and modifies.

Configuring AWS DataSync to connect to an Amazon EC2 instance would not meet the requirement of making the files available to the public for download within 30 minutes. DataSync is a service that transfers data between on-premises storage systems and AWS storage services over the internet or AWS Direct Connect. DataSync tasks can be scheduled to run at specific times or intervals, but they are not triggered by file changes.

Gateway VPC Endpoint for S3:

Create a gateway VPC endpoint for Amazon S3 in your VPC. This allows instances in your VPC to communicate with Amazon S3 without going through the internet, reducing data transfer costs and improving security.

Add Spot Instances to ECS Cluster:

Add another ECS capacity provider that uses an Auto Scaling group of Spot Instances. Configure this new capacity provider to share the load with the existing On-Demand Instances by setting an appropriate weight in the capacity provider strategy. Spot Instances offer significant cost savings compared to On-Demand Instances.

Configure Capacity Provider Strategy:

Adjust the ECS service's capacity provider strategy to utilize both On-Demand and Spot Instances effectively. This ensures a balanced distribution of tasks across both instance types, optimizing cost while maintaining availability.

By implementing a gateway VPC endpoint for S3 and incorporating Spot Instances into the ECS cluster, the company can significantly reduce operational costs without compromising on the availability or performance of the platform.

References

AWS Cost Optimization Blog on VPC Endpoints

AWS ECS Documentation on Capacity Providers

A Direct Connect gateway allows you to connect multiple VPCs across different Regions to a Direct Connect connection1. A public VIF allows you to access AWS public services such as EC21. A Site-to-Site VPN connection over the public VIF provides encryption and redundancy for the traffic between the on-premises data center and the VPCs2. This solution is cheaper than setting up additional Direct Connect connections or using a private VIF with VPC peering.

Option D is the most appropriate solution as it addresses all the specified requirements:

Amazon RDS for PostgreSQL supports native spatial data types through the PostGIS extension, making it suitable for handling spatial data migrated from Oracle databases.

AWS Schema Conversion Tool (SCT) and AWS Database Migration Service (DMS) can be used to migrate data from on-premises Oracle databases to Amazon RDS for PostgreSQL, facilitating the transition while preserving data integrity.

Cron jobs can be scheduled directly on the PostgreSQL DB instance to handle maintenance tasks, aligning with the company's existing maintenance practices.

AWS Direct Connect provides a dedicated network connection between the on-premises environment and AWS, enabling secure and efficient querying of on-premises databases as foreign tables from the RDS instance.

This solution ensures compatibility with spatial data, maintains existing maintenance workflows, and provides a secure and efficient connection to on-premises systems.

This option allows the company to use DAX to improve the performance and reduce the latency of the DynamoDB queries by caching the results in memory1. By updating the application to use DAX, the company can reduce the load on the DynamoDB tables and avoid throttling errors1. By creating an Auto Scaling group for the EC2 instances, the company can adjust the number of instances based on the demand and ensure high availability2. By creating an ALB, the company can distribute the incoming traffic across multiple EC2 instances and improve fault tolerance3. By updating the Route 53 record to use a simple routing policy that targets the ALB’s DNS alias, the company can route users to the ALB endpoint and leverage its health checks and load balancing features4. By configuring scheduled scaling for the EC2 instances before the content updates, the company can anticipate and handle traffic spikes during peak periods5.

What is Amazon DynamoDB Accelerator (DAX)?

What is Amazon EC2 Auto Scaling?

What is an Application Load Balancer?

Choosing a routing policy

Scheduled scaling for Amazon EC2 Auto Scaling

migrating the data processing script to an AWS Lambda function and using an S3 event notification to invoke the Lambda function to process the objects when the company uploads the objects. This solution meets the company's requirements of high availability and scalability, as well as reducing long-term management overhead, and is likely to be the most cost-effective option.

Amazon ECS on Fargateis ideal forevent-driven, long-running jobswith minimal management. Combine S3event notificationswithEventBridge rulesto trigger a Fargate task per upload.

Using Fargate with EventBridge

https://docs.aws.amazon.com/elasticbeanstalk/latest/dg/using-features.CNAMESwap.html

Connect to RDS outside of Lambda handler method to improve performancehttps://awstut.com/en/2022/04/30/connect-to-rds-outside-of-lambda-handler-method-to-improve-performance-en/

Using RDS Proxy, you can handle unpredictable surges in database traffic. Otherwise, these surges might cause issues due to oversubscribing connections or creating new connections at a fast rate. RDS Proxy establishes a database connection pool and reuses connections in this pool. This approach avoids the memory and CPU overhead of opening a new database connection each time. To protect the database against oversubscription, you can control the number of database connections that are created.https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/rds-proxy.html

Dis the right choice:EFS with Max I/O modeis designed formassively parallel workloadslike big data. It supports high throughput and concurrent access from many EC2 nodes across AZs.

A(Storage Gateway) is not optimized for high-performance, low-latency compute clusters.

Bis limited in performance (latency optimized for single-threaded workloads).

C(EBS) cannot be shared across AZs and is not POSIX-compatible for concurrent multi-node access.

AWS Organizations is a service that enables you to consolidate multiple AWS accounts into an organization that you create and centrally manage. By creating a management account and inviting all the existing accounts to join the organization, the solutions architect can track and consolidate expenditures for all the accounts using AWS Cost Management tools such as AWS Cost Explorer and AWS Budgets. An organizational unit (OU) is a group of accounts within an organization that can be used to apply policies and simplify management. A service control policy (SCP) is a type of policy that you can use to manage permissions in your organization. By creating an OU that includes all the development teams and applying an SCP that allows the creation of resources only in Regions that are in the United States, the solutions architect can ensure that the company meets its compliance requirements and avoids unwanted charges from other Regions. An IAM role is an identity with permission policies that determine what the identity can and cannot do in AWS. By creating an IAM role in the management account and allowing the finance team users to assume it, the solutions architect can give them access to view the Billing and Cost Management console without sharing credentials or creating additional users. References:

https://docs.aws.amazon.com/organizations/latest/userguide/orgs_introduction.html

https://docs.aws.amazon.com/organizations/latest/userguide/orgs_manage_policies_scp.html

https://docs.aws.amazon.com/IAM/latest/UserGuide/id_roles.html

https://docs.aws.amazon.com/aws-cost-management/latest/userguide/what-is-costmanagement.html