AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 118

A company needs to perform feature engineering, aggregation, and data preparation. After the features are produced, the company must implement a solution on AWS to process and store the features.

Which solution will meet these requirements?

Answer options

• A. Use Amazon SageMaker Feature Processing to process and ingest the data. Use SageMaker Feature Store to manage and store the features.
• B. Use Amazon SageMaker Model Monitor to automatically ingest and transform the data. Create an Amazon S3 bucket to store the features in JSON format.
• C. Use Amazon Managed Service for Apache Flink to transform the data and to ingest the data directly into Amazon SageMaker Feature Store. Use Feature Store to manage and store the features.
• D. Use an Amazon SageMaker batch transform job to analyze, transform, and ingest the data. Create an Amazon DynamoDB table to store the features.

Correct answer: A

Explanation

The correct answer is A because Amazon SageMaker Feature Processing is specifically designed for processing and ingesting features, while SageMaker Feature Store is built for managing and storing those features. The other options either do not utilize the appropriate tools for feature processing and storage or suggest alternatives that do not fully meet the requirements.