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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:
231
Last Updated:
Jan 23, 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 data engineer needs to optimize the performance of a data pipeline that handles retail orders. Data about the orders is ingested daily into an Amazon S3 bucket.

The data engineer runs queries once each week to extract metrics from the orders data based on the order date for multiple date ranges. The data engineer needs an optimization solution that ensures the query performance will not degrade when the volume of data increases.

Options:

A.

Partition the data based on order date. Use Amazon Athena to query the data.

B.

Partition the data based on order date. Use Amazon Redshift to query the data.

C.

Partition the data based on load date. Use Amazon EMR to query the data.

D.

Partition the data based on load date. Use Amazon Aurora to query the data.

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Question 2

A company has used an Amazon Redshift table that is named Orders for 6 months. The company performs weekly updates and deletes on the table. The table has an interleaved sort key on a column that contains AWS Regions.

The company wants to reclaim disk space so that the company will not run out of storage space. The company also wants to analyze the sort key column.

Which Amazon Redshift command will meet these requirements?

Options:

A.

VACUUM FULL Orders

B.

VACUUM DELETE ONLY Orders

C.

VACUUM REINDEX Orders

D.

VACUUM SORT ONLY Orders

Question 3

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.)

Options:

A.

Configure AWS Glue triggers to run the ETL jobs even/ hour.

B.

Use AWS Glue DataBrewto clean and prepare the data for analytics.

C.

Use AWS Lambda functions to schedule and run the ETL jobs even/ hour.

D.

Use AWS Glue connections to establish connectivity between the data sources and Amazon Redshift.

E.

Use the Redshift Data API to load transformed data into Amazon Redshift.