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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:
152
Last Updated:
Apr 29, 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 company ingests data from multiple data sources and stores the data in an Amazon S3 bucket. An AWS Glue extract, transform, and load (ETL) job transforms the data and writes the transformed data to an Amazon S3 based data lake. The company uses Amazon Athena to query the data that is in the data lake.

The company needs to identify matching records even when the records do not have a common unique identifier.

Which solution will meet this requirement?

Options:

A.

Use Amazon Made pattern matching as part of the ETL job.

B.

Train and use the AWS Glue PySpark Filter class in the ETL job.

C.

Partition tables and use the ETL job to partition the data on a unique identifier.

D.

Train and use the AWS Lake Formation FindMatches transform in the ETL job.

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Question 2

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.

Question 3

A company stores customer data in an Amazon S3 bucket. Multiple teams in the company want to use the customer data for downstream analysis. The company needs to ensure that the teams do not have access to personally identifiable information (PII) about the customers.

Which solution will meet this requirement with LEAST operational overhead?

Options:

A.

Use Amazon Macie to create and run a sensitive data discovery job to detect and remove PII.

B.

Use S3 Object Lambda to access the data, and use Amazon Comprehend to detect and remove PII.

C.

Use Amazon Kinesis Data Firehose and Amazon Comprehend to detect and remove PII.

D.

Use an AWS Glue DataBrew job to store the PII data in a second S3 bucket. Perform analysis on the data that remains in the original S3 bucket.