AWS Certified Data Engineer – Associate (DEA-C01) — Question 72

A company is migrating a legacy application to an Amazon S3 based data lake. A data engineer reviewed data that is associated with the legacy application. The data engineer found that the legacy data contained some duplicate information.
The data engineer must identify and remove duplicate information from the legacy application data.
Which solution will meet these requirements with the LEAST operational overhead?

Answer options

• A. Write a custom extract, transform, and load (ETL) job in Python. Use the DataFrame.drop_duplicates() function by importing the Pandas library to perform data deduplication.
• B. Write an AWS Glue extract, transform, and load (ETL) job. Use the FindMatches machine learning (ML) transform to transform the data to perform data deduplication.
• C. Write a custom extract, transform, and load (ETL) job in Python. Import the Python dedupe library. Use the dedupe library to perform data deduplication.
• D. Write an AWS Glue extract, transform, and load (ETL) job. Import the Python dedupe library. Use the dedupe library to perform data deduplication.

Correct answer: B

Explanation

The correct answer is B because AWS Glue's FindMatches machine learning transform is specifically designed for deduplication tasks and minimizes operational overhead by automating the process. Option A requires manual coding, which increases complexity, while options C and D also require custom implementations that do not leverage the automated capabilities of AWS Glue.