easyMultiple Choice
MLA-C01 Practice Question: An ML engineer needs to create a feature store…
An ML engineer needs to create a feature store that supports both low-latency online inference and large-scale offline training. The features are updated hourly from a streaming source. Which Amazon SageMaker Feature Store configuration should the engineer use?
⚠ Common exam trap
MLA-C01 often tests the need for both online and offline stores in a single feature group, tricking candidates into thinking separate feature groups are required for online vs. offline use cases.
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
✓
Create a feature group with both online and offline stores enabled.
A SageMaker Feature Store feature group can be configured with both an online store (for low-latency real-time inference) and an offline store (for large-scale training). This single feature group design supports both use cases and is the recommended configuration when features are updated hourly from a streaming source.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Create a feature group with both online and offline stores enabled.
Why this is correct
Enabling both online and offline stores in one feature group lets SageMaker Feature Store serve low-latency reads for real-time inference while retaining the full history in Amazon S3 for large-scale offline training, satisfying both latency and volume constraints.
- ✗
Create a feature group with only an online store enabled.
Why it's wrong here
An online-only feature group retains just the latest value in a low-latency store, so it cannot provide the large-scale historical dataset needed for offline training. It is tempting because online stores are the right choice when only real-time inference is required and no historical training data is needed.
- ✗
Create two separate feature groups: one for online and one for offline.
Why it's wrong here
Two separate feature groups split the online and offline copies, so hourly streaming ingestion must be duplicated and the stores can drift out of sync. It is tempting because separate groups suit features with genuinely different schemas or ingestion cadences, not one feature set needing both serving modes.
- ✗
Create a feature group with only an offline store enabled.
Why it's wrong here
An offline-only feature group writes records solely to Amazon S3 for training, so it cannot serve the low-latency online lookups the scenario requires. It is tempting because offline stores are the correct choice when only batch training and historical retrieval matter, with no real-time inference dependency.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.