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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:
289
Last Updated:
May 6, 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 create an empty copy of an existing table in Amazon Athena to perform data processing tasks. The existing table in Athena contains 1,000 rows.

Which query will meet this requirement?

Options:

A.

CREATE TABLE new_table LIKE old_table;

B.

CREATE TABLE new_table AS SELECT * FROM old_table WITH NO DATA;

C.

CREATE TABLE new_table AS SELECT * FROM old_table;

D.

CREATE TABLE new_table AS SELECT * FROM old_table WHERE 1=1;

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Question 2

A media company uploads large video files to Amazon S3 for processing. After processing, the company needs to keep the original files for 90 days in case the files require reprocessing. After 90 days, the company can delete the files to reduce storage costs. The company stores the processed videos in a different S3 bucket.

Which S3 Lifecycle configuration will meet these requirements for the original files MOST cost-effectively?

Options:

A.

Store the files in S3 Standard for 90 days. Transition the files to S3 Glacier Flexible Retrieval for long-term storage. Then expire the files.

B.

Store the files in S3 Standard for 90 days. Enable versioning. Enable Object Lock on the files for 90 days. Then expire the files.

C.

Store the files in S3 Standard for 90 days. Implement S3 Lifecycle management to expire the files.

D.

Store the files in S3 Intelligent-Tiering for 90 days. Enable versioning. Add S3 Lifecycle management to expire the files.

Question 3

A manufacturing company uses AWS Glue jobs to process IoT sensor data to generate predictive maintenance models. A data engineer needs to implement automated data quality checks to identify temperature readings that are outside the expected range of -50°C to 150°C. The data quality checks must also identify records that are missing timestamp values.

The data engineer needs a solution that requires minimal coding and can automatically flag the specified issues.

Which solution will meet these requirements?

Options:

A.

Create an AWS Glue DataBrew project to profile the sensor data. Define completeness rules for timestamps. Set up numeric range validation for temperature values.

B.

Use AWS Glue ' s Data Quality rules and machine learning (ML)-based anomaly detection to identify missing timestamps and to detect temperature anomalies.

C.

Create an AWS Lambda function to scan the sensor data files to validate temperature ranges. Use AWS Glue Data Catalog tables to check timestamp completeness.

D.

Create an AWS Glue DynamicFrame that uses a custom data quality operator to profile the sensor data. Use Amazon SageMaker Data Wrangler transforms to validate timestamps and temperature ranges.