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Q11 Single choice

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.

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