easyMultiple Choice
MLA-C01 Practice Question: A data scientist wants to track feature…
A data scientist wants to track feature definitions, share them across teams, and serve features for both training and real-time inference. Which AWS service provides these capabilities?
⚠ Common exam trap
Test-takers frequently confuse a general-purpose storage or catalog service (like S3 or Glue Data Catalog) with a purpose-built ML feature store, overlooking the need for both offline and online serving with feature-specific management.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Amazon SageMaker Feature Store
Amazon SageMaker Feature Store is purpose-built for ML workflows, providing a centralized repository to define, share, and serve features for both training (batch) and real-time inference (low-latency retrieval). It supports offline and online stores, enabling consistent feature definitions across teams and automatic feature ingestion via SageMaker Pipelines or custom code.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Amazon DynamoDB
Why it's wrong here
DynamoDB provides low-latency key-value lookups but holds no feature registry, lineage or versioning, so definitions cannot be shared or tracked across teams. It is the correct choice as the online store backing a feature store's serving layer, not as the service that manages feature definitions itself.
- ✗
Amazon S3
Why it's wrong here
Amazon S3 stores objects and cannot register feature definitions, share them across teams, or serve features consistently for training and real-time inference. It is tempting because it commonly holds training datasets, and it would be correct as the underlying storage layer feeding a feature store.
- ✗
AWS Glue Data Catalog
Why it's wrong here
Glue Data Catalog holds table and schema metadata for analytics, but it does not store feature values or expose a low-latency endpoint for real-time inference. It is the right choice when cataloguing datasets for Athena, EMR or Redshift queries, not for tracking feature definitions and serving them to models.
- ✓
Amazon SageMaker Feature Store
Why this is correct
Amazon SageMaker Feature Store provides a centralised repository that stores feature definitions with metadata, enabling discovery and reuse across teams. It supports both online and offline stores, satisfying the stem's dual requirement: low-latency retrieval for real-time inference and bulk access for training datasets.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.