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Answer: Implement custom data quality rules in DataBrew. Apply the custom rules across datasets.
Option B is CORRECT because AWS Glue DataBrew allows users to create custom data quality rules, which can include definitions for detecting non-standard PII categories. Once created, these rules can be applied consistently across multiple datasets, automating the detection process while minimizing operational overhead. This approach integrates seamlessly with the existing DataBrew workflows.
Author: Ritesh Yadav
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Question 33/58
A company analyzes data in a data lake every quarter to perform inventory assessments. A data engineer uses AWS Glue DataBrew to detect any personally identifiable formation (PII) about customers within the data. The company's privacy policy considers some custom categories of information to be PII. However, the categories are not included in standard DataBrew data quality rules.
The data engineer needs to modify the current process to scan for the custom PII categories across multiple datasets within the data lake.
Which solution will meet 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.
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