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Amazon Web Services Data-Engineer-Associate Exam With Confidence Using Practice Dumps

Exam Code:
Data-Engineer-Associate
Exam Name:
AWS Certified Data Engineer - Associate (DEA-C01)
Questions:
302
Last Updated:
Aug 7, 2026
Exam Status:
Stable
Amazon Web Services Data-Engineer-Associate

Data-Engineer-Associate: AWS Certified Data Engineer Exam 2025 Study Guide Pdf and Test Engine

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AWS Certified Data Engineer - Associate (DEA-C01) Questions and Answers

Question 1

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?

Options:

A.

Turn on concurrency scaling in workload management (WLM) for Redshift Serverless workgroups.

B.

Turn on concurrency scaling at the workload management (WLM) queue level in the Redshift cluster.

C.

Turn on concurrency scaling in the settings during the creation of and new Redshift cluster.

D.

Turn on concurrency scaling for the daily usage quota for the Redshift cluster.

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Question 2

A company needs to implement a workflow to process transactions. Each transaction goes through multiple levels of validation. Each validation level depends on the preceding validation level.

The workflow must either process or reject each transaction within 24 hours. The workflow must run for less than 24 hours total.

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

Options:

A.

Create a standard workflow in AWS Step Functions. Implement a Wait for Callback pattern to wait for the validation steps to finish.

B.

Create an express workflow in AWS Step Functions. Implement a Wait for Callback pattern to wait for the validation steps to finish.

C.

Use AWS Lambda functions to implement the workflow. Use Amazon EventBridge to invoke the validation steps.

D.

Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to implement the workflow.

Question 3

A data engineer must manage the ingestion of real-time streaming data into AWS. The data engineer wants to perform real-time analytics on the incoming streaming data by using time-based aggregations over a window of up to 30 minutes. The data engineer needs a solution that is highly fault tolerant.

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

Options:

A.

Use an AWS Lambda function that includes both the business and the analytics logic to perform time-based aggregations over a window of up to 30 minutes for the data in Amazon Kinesis Data Streams.

B.

Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to analyze the data that might occasionally contain duplicates by using multiple types of aggregations.

C.

Use an AWS Lambda function that includes both the business and the analytics logic to perform aggregations for a tumbling window of up to 30 minutes, based on the event timestamp.

D.

Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to analyze the data by using multiple types of aggregations to perform time-based analytics over a window of up to 30 minutes.