Explanation:
Option B is CORRECT because creating an AWS Lambda function that makes an API call to AWS Glue Data Quality allows the data engineer to automate the process of updating the data quality rules across all 1,000 AWS Glue Data Catalog tables. This solution is highly scalable and can make the necessary changes programmatically, significantly reducing manual effort and operational overhead. The Lambda function can loop through the tables and apply the changes efficiently, ensuring consistency across all the tables.
Ultimate access to all questions.
Question 14/58
A data engineer has implemented data quality rules in 1,000 AWS Glue Data Catalog tables. Because of a recent change in business requirements, the data engineer must edit the data quality rules.
How should the data engineer meet this requirement with the LEAST operational overhead?
A
Create a pipeline in AWS Glue ETL to edit the rules for each of the 1,000 Data Catalog tables. Use an AWS Lambda function to call the corresponding AWS Glue job for each Data Catalog table.
B
Create an AWS Lambda function that makes an API call to AWS Glue Data Quality to make the edits.
C
Create an Amazon EMR cluster. Run a pipeline on Amazon EMR that edits the rules for each Data Catalog table. Use an AWS Lambda function to run the EMR pipeline.
D
Use the AWS Management Console to edit the rules within the Data Catalog.
No comments yet.