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Amazon Web Services Data-Engineer-Associate Exam Dumps - Actual Questions Answers


Data-Engineer-Associate practice test questions answers

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  • Last Update: May 14, 2026
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AWS Certified Data Engineer - Associate (DEA-C01) Exam Prep: Professional Practice and Learning Suite

Don't just remember AWS services. Master Data Orchestration with Explanations Checked by Experts.

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The AWS Certified Data Engineer - Associate (DEA-C01) exam is still one of the most technical associate-level certifications in 2026. To pass, you need to know a lot about Data Ingestion, Transformation, Store Management, and Governance.

We give you more than just a list of answers at DumpsMate. AWS Solutions Architects have created a Professional Learning Methodology that we offer. Our practice sets make sure you know how to build scalable, cost-effective data pipelines, which will make you "Job-Ready" and help you pass your exam on the first try.

DumpsMate Premium vs. Standard AWS Dumps

Dumps Compare explains why our Data Engineer Associate material is the best choice for people who work in the cloud:

Feature

Generic AWS "Dumps"

DumpsMate Premium Suite

Architectural Logic

No reasoning provided.

Detailed "Why" and "Why Not" logic for every scenario.

AWS Best Practices

Often suggests outdated methods.

Aligned with the AWS Well-Architected Framework.

Objective Mapping

Randomly organized.

100% Mapped to the 4 AWS Exam Domains.

Reference Material

None.

Citations from AWS Documentation and Whitepapers.

Complex Scenarios

Simple one-liners.

Multi-service scenarios (Glue + Redshift + Athena).

Update Status

Stale content.

Updated for May 2026 AWS Blueprint changes.

Master the 4 Domains of the DEA-C01 Exam

Our practice engine makes sure that you cover 100% of the official AWS Data Engineer Associate syllabus. We make the heavy data lifting easier to understand by breaking it up into clear modules:

  • Domain 1: Data Ingestion and Transformation (34%): Learn how to use AWS Glue, Amazon Kinesis, and AWS Lambda for both real-time and batch processing.
  • Domain 2: Data Store Management (26%): Learn all about Amazon S3, Redshift, Lake Formation, and DynamoDB indexing strategies.
  • Domain 3: Data Operations and Support (22%): using CloudWatch to keep an eye on things, Step Functions to coordinate things, and finding ways to save money.
  • Domain 4: Data Security and Governance (18%): AWS KMS encryption, IAM policies, and Macie for finding sensitive data.

Real-Style Learning: Example Question and Expert Reasoning

Data-Engineer-Associate Questions and Answers

Question # 1

A company wants to migrate a data warehouse from Teradata to Amazon Redshift. Which solution will meet this requirement with the LEAST operational effort?

A.

Use AWS Database Migration Service (AWS DMS) Schema Conversion to migrate the schema. Use AWS DMS to migrate the data.

B.

Use the AWS Schema Conversion Tool (AWS SCT) to migrate the schema. Use AWS Database Migration Service (AWS DMS) to migrate the data.

C.

Use AWS Database Migration Service (AWS DMS) to migrate the data. Use automatic schema conversion.

D.

Manually export the schema definition from Teradata. Apply the schema to the Amazon Redshift database. Use AWS Database Migration Service (AWS DMS) to migrate the data.

Question # 2

A company uses a variety of AWS and third-party data stores. The company wants to consolidate all the data into a central data warehouse to perform analytics. Users need fast response times for analytics queries.

The company uses Amazon QuickSight in direct query mode to visualize the data. Users normally run queries during a few hours each day with unpredictable spikes.

Which solution will meet these requirements with the LEAST operational overhead?

A.

Use Amazon Redshift Serverless to load all the data into Amazon Redshift managed storage (RMS).

B.

Use Amazon Athena to load all the data into Amazon S3 in Apache Parquet format.

C.

Use Amazon Redshift provisioned clusters to load all the data into Amazon Redshift managed storage (RMS).

D.

Use Amazon Aurora PostgreSQL to load all the data into Aurora.

Question # 3

A company uses AWS Glue Data Catalog to index data that is uploaded to an Amazon S3 bucket every day. The company uses a daily batch processes in an extract, transform, and load (ETL) pipeline to upload data from external sources into the S3 bucket.

The company runs a daily report on the S3 data. Some days, the company runs the report before all the daily data has been uploaded to the S3 bucket. A data engineer must be able to send a message that identifies any incomplete data to an existing Amazon Simple Notification Service (Amazon SNS) topic.

Which solution will meet this requirement with the LEAST operational overhead?

A.

Create data quality checks for the source datasets that the daily reports use. Create a new AWS managed Apache Airflow cluster. Run the data quality checks by using Airflow tasks that run data quality queries on the columns data type and the presence of null values. Configure Airflow Directed Acyclic Graphs (DAGs) to send an email notification that informs the data engineer about the incomplete datasets to the SNS topic.

B.

Create data quality checks on the source datasets that the daily reports use. Create a new Amazon EMR cluster. Use Apache Spark SQL to create Apache Spark jobs in the EMR cluster that run data quality queries on the columns data type and the presence of null values. Orchestrate the ETL pipeline by using an AWS Step Functions workflow. Configure the workflow to send an email notification that informs the data engineer about the incomplete da

C.

Create data quality checks on the source datasets that the daily reports use. Create data quality actions by using AWS Glue workflows to confirm the completeness and consistency of the datasets. Configure the data quality actions to create an event in Amazon EventBridge if a dataset is incomplete. Configure EventBridge to send the event that informs the data engineer about the incomplete datasets to the Amazon SNS topic.

D.

Create AWS Lambda functions that run data quality queries on the columns data type and the presence of null values. Orchestrate the ETL pipeline by using an AWS Step Functions workflow that runs the Lambda functions. Configure the Step Functions workflow to send an email notification that informs the data engineer about the incomplete datasets to the SNS topic.

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