AWS Certified Data Engineer - Associate DEA-C01Demo

By Amazon · Question Mode

QuestionQ157

Data Operations and Support

A company operates a data platform on AWS. The platform uses AWS Glue to supply a data catalog and perform processing. The company has identified data quality issues.

The company must implement data quality validations. The validations must include rules for known issues and must be able to automatically identify unexpected data quality issues.

Which solution meets these requirements with the LEAST operational overhead?

  • A Use AWS Glue jobs to implement AWS Glue Data Quality validations that include anomaly detection.
  • B Use AWS Glue jobs to implement data quality rules that use open source data quality frameworks.
  • C Use AWS Glue DataBrew to profile the data. Configure data quality rules based on the data quality results from the profiling.
  • D Use AWS Glue jobs to implement data quality validations that use SQL statements.
Explanation

AWS Glue Data Quality provides managed, serverless DQDL rules to validate known data-quality conditions and ML-based anomaly detection in AWS Glue ETL jobs to identify unexpected or hard-to-detect issues. This avoids the operational burden of maintaining custom SQL checks or open-source data-quality frameworks.

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