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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:
Questions:
187
Last Updated:
Jul 12, 2025
Exam Status:
Stable
Amazon Web Services Data-Engineer-Associate

Data-Engineer-Associate: AWS Certified Associate 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 build an extract, transform, and load (ETL) job. The ETL job will process daily incoming .csv files that users upload to an Amazon S3 bucket. The size of each S3 object is less than 100 MB.

Which solution will meet these requirements MOST cost-effectively?

Options:

A.

Write a custom Python application. Host the application on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster.

B.

Write a PySpark ETL script. Host the script on an Amazon EMR cluster.

C.

Write an AWS Glue PySpark job. Use Apache Spark to transform the data.

D.

Write an AWS Glue Python shell job. Use pandas to transform the data.

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Question 2

A company is setting up a data pipeline in AWS. The pipeline extracts client data from Amazon S3 buckets, performs quality checks, and transforms the data. The pipeline stores the processed data in a relational database. The company will use the processed data for future queries.

Which solution will meet these requirements MOST cost-effectively?

Options:

A.

Use AWS Glue ETL to extract the data from the S3 buckets and perform the transformations. Use AWS Glue Data Quality to enforce suggested quality rules. Load the data and the quality check results into an Amazon RDS for MySQL instance.

B.

Use AWS Glue Studio to extract the data from the S3 buckets. Use AWS Glue DataBrew to perform the transformations and quality checks. Load the processed data into an Amazon RDS for MySQL instance. Load the quality check results into a new S3 bucket.

C.

Use AWS Glue ETL to extract the data from the S3 buckets and perform the transformations. Use AWS Glue DataBrew to perform quality checks. Load the processed data and the quality check results into a new S3 bucket.

D.

Use AWS Glue Studio to extract the data from the S3 buckets. Use AWS Glue DataBrew to perform the transformations and quality checks. Load the processed data and quality check results into an Amazon RDS for MySQL instance.

Question 3

A data engineer develops an AWS Glue Apache Spark ETL job to perform transformations on a dataset. When the data engineer runs the job, the job returns an error that reads, "No space left on device."

The data engineer needs to identify the source of the error and provide a solution.

Which combinations of steps will meet this requirement MOST cost-effectively? (Select TWO.)

Options:

A.

Scale out the workers vertically to address data skewness.

B.

Use the Spark UI and AWS Glue metrics to monitor data skew in the Spark executors.

C.

Scale out the number of workers horizontally to address data skewness.

D.

Enable the --write-shuffle-files-to-s3 job parameter. Use the salting technique.

E.

Use error logs in Amazon CloudWatch to monitor data skew.