Exam AWS Certified Data Engineer - Associate DEA-C01 topic 1 question 225 discussion

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

Question #: 225
Topic #: 1

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

A company builds a new data pipeline to process data for business intelligence reports. Users have noticed that data is missing from the reports.

A data engineer needs to add a data quality check for columns that contain null values and for referential integrity at a stage before the data is added to storage.

Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon SageMaker Data Wrangler to create a Data Quality and Insights report.
B. Use AWS Glue ETL jobs to perform a data quality evaluation transform on the data. Use an IsComplete rule on the requested columns. Use a ReferentialItegrity rule for each join.
C. Use AWS Glue ETL jobs to perform a SQL transform on the data to determine whether requested column contain null values. Use a second SQL transform to check referential integrity.
D. Use Amazon SageMaker Data Wrangler and a custom Python transform to create custom rules to check for null values and referential integrity.

Show Suggested Answer

Suggested Answer: B 🗳️

by AnsonCert at June 7, 2025, 9:49 a.m.

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AnsonCert

6 days, 8 hours ago

Selected Answer: B

Glue build-in Data Quality Definition Language (DQDL) supports: IsComplete to check columns are non-null or not, ReferentialIntegrity to check columns' existance in another data table (Join relationship check) https://aws.amazon.com/glue/faqs/#topic-3 Glue's Data Quality transform is serverless and supprt integration with Spark/ETL workflows - Least operational overhead.

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