Task 3.4: Ensure Data Quality DEA-C01 Practice Questions (Page 4)

Questions 16–18

1. 16foundation · easy

   During an AWS Glue ETL job, a data engineer wants to ensure that records with missing required fields are not written to the target table. Which approach directly validates data quality during the processing step?

   • AConfigure an AWS Glue DataBrew recipe to clean the data after it is loaded into the target table.
   • BUse an AWS Glue crawler to infer the schema and automatically reject records with missing fields.
   • CEnable versioning on the target Amazon S3 bucket to preserve the original data for later validation.
   • DAdd a filter or conditional check in the ETL script to drop records with empty required fields before writing.
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2. 17foundation · easy

   A data engineer is using AWS Glue DataBrew to investigate why the same customer appears with different email addresses in two source datasets. Which DataBrew feature is most appropriate for comparing the datasets to identify these inconsistencies?

   • AData profiling
   • BData lineage
   • CDataBrew joins
   • DData quality rules
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3. 18expert · hard

   A healthcare company ingests patient data from multiple hospitals. Each hospital provides data in different formats, and some records have missing or invalid values. The company needs to ensure that all data meets strict quality rules before it is used for analytics. They also need to document the rules for auditors. Which approach should they take?

   • AWrite custom Python scripts in AWS Glue ETL to validate each dataset and store the rules in a text file.
   • BUse Amazon Athena to run queries that check for invalid data and save the queries as documentation.
   • CUse AWS Config to define rules for data quality.
   • DUse AWS Glue DataBrew to create data quality rules and apply them to all datasets, and export the rules for documentation.
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