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DEA-C01 Exam Practice Questions

AWS Certified Data Engineer – Associate

DEA-C01 English PDF practice questions: 366 questions in source order, with options supported by the source format, answers and available explanations. Access covers this exam only. One-time payment; no automatic renewal.

Exam overview

For data engineers implementing ingestion, transformations, storage, operational pipelines and data governance.

Exam code
DEA-C01
Official exam / credential
AWS Certified Data Engineer – Associate
Exam format
Single-answer and multiple-response questions
Exam duration
130 minutes
Official exam questions
65 questions
Official exam languages
English, Japanese, Korean, Simplified Chinese

Official exam languages and practice-question file languages are listed separately. Confirm the exam version and available languages when booking with the provider.

Official sources: Provider reference
Information checked: 2026-10-20

Supported languages

  • English
  • Simplified Chinese
Try before you buy

First 20 practice questions

366 total questions

Questions follow the source order. Expand each answer to see its explanation.

Disclaimer: All questions on ExamPico are compiled from internet sources and are provided for study reference only.

Question 16

Question#16Topic1

A company uses an on-premises Microsoft SQL Server database to store financial transaction data. The company migrates the transaction data from the on-premises database to AWS at the end of each month. The company has noticed that the cost to migrate data from the on-premises database to an Amazon RDS for SQL Server database has increased recently.
The company requires a cost-effective solution to migrate the data to AWS. The solution must cause minimal downtown for the applications that access the database.
Which AWS service should the company use to meet these requirements?

  1. A. AWS Lambda
  2. B. AWS Database Migration Service (AWS DMS)
  3. C. AWS Direct Connect
  4. D. AWS DataSync
Show answer and explanation

Answer

B

Explanation

AWS Database Migration Service (DMS) is specifically designed for migrating data from various sources, including on-premises databases, to AWS with minimal downtime and disruption to applications. It supports homogeneous migrations (e.g., SQL Server to SQL Server) as well as heterogeneous migrations (e.g., SQL Server to Amazon RDS for SQL Server).

Question 17

Question#17Topic1

A data engineer is building a data pipeline on AWS by using AWS Glue extract, transform, and load (ETL) jobs. The data engineer needs to process data from Amazon RDS and MongoDB, perform transformations, and load the transformed data into Amazon Redshift for analytics. The data updates must occur every hour.
Which combination of tasks will meet these requirements with the LEAST operational overhead? (Choose two.)

  1. A. Configure AWS Glue triggers to run the ETL jobs every hour.
  2. B. Use AWS Glue DataBrew to clean and prepare the data for analytics.
  3. C. Use AWS Lambda functions to schedule and run the ETL jobs every hour.
  4. D. Use AWS Glue connections to establish connectivity between the data sources and Amazon Redshift.
  5. E. Use the Redshift Data API to load transformed data into Amazon Redshift.
Show answer and explanation

Answer

AD

Explanation

A - this is obvious and D -https://docs.aws.amazon.com/glue/latest/dg/console-connections.html

Question 18

Question#18Topic1

A company uses an Amazon Redshift cluster that runs on RA3 nodes. The company wants to scale read and write capacity to meet demand. A data engineer needs to identify a solution that will turn on concurrency scaling.
Which solution will meet this requirement?

  1. A. Turn on concurrency scaling in workload management (WLM) for Redshift Serverless workgroups.
  2. B. Turn on concurrency scaling at the workload management (WLM) queue level in the Redshift cluster.
  3. C. Turn on concurrency scaling in the settings during the creation of any new Redshift cluster.
  4. D. Turn on concurrency scaling for the daily usage quota for the Redshift cluster.
Show answer and explanation

Answer

B

Explanation

Concurrency scaling in Amazon Redshift allows the cluster to automatically add and remove compute resources in response to workload demands. Enabling concurrency scaling at the workload management (WLM) queue level allows you to specify which queues can benefit from concurrency scaling based on the query workload.

Question 19

Question#19Topic1

A data engineer must orchestrate a series of Amazon Athena queries that will run every day. Each query can run for more than 15 minutes.
Which combination of steps will meet these requirements MOST cost-effectively? (Choose two.)

  1. A. Use an AWS Lambda function and the Athena Boto3 client start_query_execution API call to invoke the Athena queries programmatically.
  2. B. Create an AWS Step Functions workflow and add two states. Add the first state before the Lambda function. Configure the second state as a Wait state to periodically check whether the Athena query has finished using the Athena Boto3 get_query_execution API call. Configure the workflow to invoke the next query when the current query has finished running.
  3. C. Use an AWS Glue Python shell job and the Athena Boto3 client start_query_execution API call to invoke the Athena queries programmatically.
  4. D. Use an AWS Glue Python shell script to run a sleep timer that checks every 5 minutes to determine whether the current Athena query has finished running successfully. Configure the Python shell script to invoke the next query when the current query has finished running.
  5. E. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the Athena queries in AWS Batch.
Show answer and explanation

Answer

AB

Explanation

A. Lambda is a good option and it only trigger the athena not actually run it. No need 15 min for it. B. it mentioned a series of athena queries and it may means that one query should wait until the former one finished. B is the perfect way to do it. And lambda and step functions are very cost effective.

Question 20

Question#20Topic1

A company is migrating on-premises workloads to AWS. The company wants to reduce overall operational overhead. The company also wants to explore serverless options.
The company's current workloads use Apache Pig, Apache Oozie, Apache Spark, Apache Hbase, and Apache Flink. The on-premises workloads process petabytes of data in seconds. The company must maintain similar or better performance after the migration to AWS.
Which extract, transform, and load (ETL) service will meet these requirements?

  1. A. AWS Glue
  2. B. Amazon EMR
  3. C. AWS Lambda
  4. D. Amazon Redshift
Show answer and explanation

Answer

B

Explanation

Glue is like the more good-looking one, but weaker brother of EMR. So when it's about petabyte scales, let EMR do the work and have Glue stay away from the action.

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