A company uses Amazon DataZone as a data governance and business catalog solution. The company stores data in an Amazon S3 data lake. The company uses AWS Glue with an AWS Glue Data Catalog. A data…

Question

Options

• ACreate a data quality ruleset with Data Quality Definition language (DQDL) rules that apply to a
• BConfigure AWS Glue ETL jobs to use an Evaluate Data Quality transform. Define a data quality
• CCreate a data quality ruleset with Data Quality Definition language (DQDL) rules that apply to a
• DConfigure AWS Glue ETL jobs to use an Evaluate Data Quality transform. Define a data quality

• A
  3% (1)
• B
  12% (4)
• C
  79% (27)
• D
  6% (2)

Explanation

Option C involves creating a data quality ruleset using DQDL (Data Quality Definition Language) rules applied directly to an AWS Glue Data Catalog table (rather than to an ETL job transform), and then configuring the ruleset to publish results to Amazon DataZone. This approach uses a standalone data quality ruleset-not embedded inside an ETL job-which allows quality evaluations to be run and published independently of ETL pipelines, making it the correct integration path for surfacing scores in DataZone's business catalog. Options B and D use the 'Evaluate Data Quality' transform inside a Glue ETL job, which publishes results to CloudWatch or S3 but not natively to DataZone. Option A uses DQDL on a Data Catalog table but likely lacks the DataZone publishing configuration.

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