MLA-C01 Question #135: Real Exam Question with Answer & Explanation

Question

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?

Options

• AUse Amazon SageMaker Feature Processing to process and ingest the data. Use SageMaker
• BUse Amazon SageMaker Model Monitor to automatically ingest and transform the data. Create an
• CUse Amazon Managed Service for Apache Flink to transform the data and to ingest the data
• DUse an Amazon SageMaker batch transform job to analyze, transform, and ingest the data.

Explanation

Option A is correct because Amazon SageMaker Feature Store is AWS's purpose-built solution for exactly this workflow - it ingests engineered features, stores them in both an online store (low-latency retrieval) and offline store (batch training), and manages feature groups for reuse across ML pipelines.

Why the distractors are wrong:

• B (Model Monitor) is designed to detect data drift and quality issues in deployed models, not to ingest or transform raw data into features.
• C (Managed Service for Apache Flink) is a real-time streaming analytics service; while it can transform data, it has no feature store capability and isn't the right fit for ML feature management.
• D (Batch Transform) runs inference on a trained model against a large dataset - it's for predictions, not feature engineering or feature storage.

Memory tip: Think "Feature Store = Feature Engineering + Storage." Whenever an exam question mentions producing, storing, or reusing ML features, SageMaker Feature Store is almost always the answer - it's the only AWS service with a dedicated online/offline store architecture built specifically for ML feature management.

No community discussion yet for this question.