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Amazon-DEA-C01 Exam - Topic 2 Question 33 Discussion

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?
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 ReferentialIntegrity rule for each join.
A) Use Amazon SageMaker Data Wrangler to create a Data Quality and Insights report.
C) Use AWS Glue ETL jobs to perform a SQL transform on the data to determine whether requested columns 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.

Amazon-DEA-C01 Exam - Topic 2 Question 33 Discussion

Actual exam question for Amazon's Amazon-DEA-C01 exam
Question #: 33
Topic #: 2
[All Amazon-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?

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Suggested Answer: B

AWS Glue Data Quality transforms allow you to define built-in rules like IsComplete for null validation and ReferentialIntegrity for relationship validation---all with minimal code and operational overhead.

''Use AWS Glue Data Quality rules such as IsComplete and ReferentialIntegrity within ETL jobs to automatically validate incoming data.''


by Rozella at Jul 19, 2026, 12:28 PM

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