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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 18, 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 wants to orchestrate a set of extract, transform, and load (ETL) jobs that run on AWS. The ETL jobs contain tasks that must run Apache Spark jobs on Amazon EMR, make API calls to Salesforce, and load data into Amazon Redshift.

The ETL jobs need to handle failures and retries automatically. The data engineer needs to use Python to orchestrate the jobs.

Which service will meet these requirements?

Options:

A.

Amazon Managed Workflows for Apache Airflow (Amazon MWAA)

B.

AWS Step Functions

C.

AWS Glue

D.

Amazon EventBridge

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Question 2

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.

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.

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