AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 43

A company is using an Amazon Redshift database as its single data source. Some of the data is sensitive.
A data scientist needs to use some of the sensitive data from the database. An ML engineer must give the data scientist access to the data without transforming the source data and without storing anonymized data in the database.
Which solution will meet these requirements with the LEAST implementation effort?

Answer options

• A. Configure dynamic data masking policies to control how sensitive data is shared with the data scientist at query time.
• B. Create a materialized view with masking logic on top of the database. Grant the necessary read permissions to the data scientist.
• C. Unload the Amazon Redshift data to Amazon S3. Use Amazon Athena to create schema-on-read with masking logic. Share the view with the data scientist.
• D. Unload the Amazon Redshift data to Amazon S3. Create an AWS Glue job to anonymize the data. Share the dataset with the data scientist.

Correct answer: A

Explanation

The correct answer is A because dynamic data masking allows for real-time control of sensitive data access without changing the data structure or storing anonymized versions. The other options require additional implementation efforts like creating views or exporting data, which complicates the process and does not meet the requirement of minimal effort.