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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)
Certification:
AWS Certified Data Engineer
Vendor:
Amazon Web Services
Questions:
289
Last Updated:
May 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

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Question 1

A data engineer uses Amazon Kinesis Data Streams to ingest and process records that contain user behavior data from an application every day.

The data engineer notices that the data stream is experiencing throttling because hot shards receive much more data than other shards in the data stream.

How should the data engineer resolve the throttling issue?

Options:

A.

Use a random partition key to distribute the ingested records.

B.

Increase the number of shards in the data stream. Distribute the records across the shards.

C.

Limit the number of records that are sent each second by the producer to match the capacity of the stream.

D.

Decrease the size of the records that the producer sends to match the capacity of the stream.

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Question 2

A company has a data processing pipeline that runs multiple SQL queries in sequence against an Amazon Redshift cluster. After a merger, a query joining two large sales tables becomes slow. Table S1 has 10 billion records, Table S2 has 900 million records.

The query performance must improve.

Options:

A.

Use the KEY distribution style for both sales tables. Select a low cardinality column to use for the join.

B.

Use the KEY distribution style for both sales tables. Select a high cardinality column to use for the join.

C.

Use the EVEN distribution style for Table S1. Use the ALL distribution style for Table S2.

D.

Use the Amazon Redshift query optimizer to review and select optimizations to implement.

E.

Use Amazon Redshift Advisor to review and select optimizations to implement.

Question 3

A company extracts approximately 1 TB of data every day from data sources such as SAP HANA, Microsoft SQL Server, MongoDB, Apache Kafka, and Amazon DynamoDB. Some of the data sources have undefined data schemas or data schemas that change.

A data engineer must implement a solution that can detect the schema for these data sources. The solution must extract, transform, and load the data to an Amazon S3 bucket. The company has a service level agreement (SLA) to load the data into the S3 bucket within 15 minutes of data creation.

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

Options:

A.

Use Amazon EMR to detect the schema and to extract, transform, and load the data into the S3 bucket. Create a pipeline in Apache Spark.

B.

Use AWS Glue to detect the schema and to extract, transform, and load the data into the S3 bucket. Create a pipeline in Apache Spark.

C.

Create a PvSpark proqram in AWS Lambda to extract, transform, and load the data into the S3 bucket.

D.

Create a stored procedure in Amazon Redshift to detect the schema and to extract, transform, and load the data into a Redshift Spectrum table. Access the table from Amazon S3.

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