AWS Certified Data Engineer - Associate DEA-C01Demo

By Amazon · Question Mode

QuestionQ298

Data Security and Governance

A company reviews data in a data lake each quarter to conduct inventory assessments. A data engineer uses AWS Glue DataBrew to identify personally identifiable information (PII) about customers in the data. The company’s privacy policy treats certain custom information categories as PII. However, these categories are not covered by the standard DataBrew data quality rules.

The data engineer must update the existing process to scan multiple datasets in the data lake for the custom PII categories.

Which solution meets these requirements with the LEAST operational overhead?

  • A Manually review the data for custom PII categories.
  • B Implement custom data quality rules in DataBrew. Apply the custom rules across datasets.
  • C Develop custom Python scripts to detect the custom PII categories. Call the scripts from DataBrew.
  • D Implement regex patterns to extract PII information from fields during extract transform, and load (ETL) operations into the data lake.
Explanation

AWS Glue DataBrew supports creating custom data quality rulesets to detect organization-specific PII categories. These rules can be applied consistently across multiple datasets without custom code, reducing ongoing maintenance. This approach requires less operational overhead than manual reviews, custom Python scripts, or complex regex patterns embedded in ETL workflows.

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