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Answer: Create custom AWS Glue Data Quality rulesets to define specific data quality checks.
Option B is CORRECT because creating custom AWS Glue Data Quality rulesets allows you to define specific data quality checks tailored to your dataset's validation requirements. This approach ensures that the validation rules accurately address the company's needs for data accuracy and consistency during the ETL process.
Author: Ritesh Yadav
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Question 28/60
A retail company uses AWS Glue for extract, transform, and load (ETL) operations on a dataset that contains information about customer orders. The company wants to implement specific validation rules to ensure data accuracy and consistency.
Which solution will meet these requirements?
A
Use AWS Glue job bookmarks to track the data for accuracy and consistency.
B
Create custom AWS Glue Data Quality rulesets to define specific data quality checks.
C
Use the built-in AWS Glue Data Quality transforms for standard data quality validations.
D
Use AWS Glue Data Catalog to maintain a centralized data schema and metadata repository.
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