Exam AWS Certified Data Engineer - Associate DEA-C01 topic 1 question 226 discussion

Exam question from Amazon's AWS Certified Data Engineer - Associate DEA-C01

Question #: 226
Topic #: 1

[All AWS Certified Data Engineer - Associate DEA-C01 Questions]

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?

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.

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Suggested Answer: A 🗳️

by rdiaz at July 4, 2025, 4:43 a.m.

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rdiaz

1 month, 2 weeks ago

Selected Answer: A

AWS Glue ETL provides a serverless, scalable, and cost-effective way to extract and transform data from S3. • AWS Glue Data Quality is natively integrated into Glue ETL and is designed for automated, rule-based data validation, with low operational overhead. • Amazon RDS for MySQL is a cost-effective managed relational database suitable for structured data and querying. • This option keeps all steps within a unified serverless environment, minimizing costs and complexity.

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