MLA-C01 Practice Question: A machine learning team is building a feature…

A machine learning team is building a feature store using Amazon SageMaker Feature Store. They need to store features that support both real-time inference (low latency) and historical training. Which configuration should they choose?

⚠ Common exam trap

MLA-C01 often tests the misconception that you need separate feature groups for online and offline, when a single feature group can enable both stores.

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

Amazon SageMaker Feature Store allows a single feature group to have both an online store and an offline store enabled. The online store provides low-latency access for real-time inference, while the offline store stores historical data in S3 for training and batch scoring. This configuration satisfies both requirements without duplicating feature groups.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

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Create two separate feature groups: one online and one offline
Why it's wrong here
Two separate feature groups split the feature definitions, so online and offline records diverge and point-in-time consistency breaks. A single feature group with both stores enabled is the intended design; separate groups suit unrelated feature sets with different schemas.
✓
Create a feature group with both online and offline stores enabled
Why this is correct
Enabling both online and offline stores on the feature group writes records to a low-latency online store for real-time inference while also landing them in Amazon S3 for historical training queries, satisfying the dual latency and training requirements in one configuration.
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Create a feature group with only an online store enabled
Why it's wrong here
An online-only feature group retains just the low-latency record store, so no historical data is written to Amazon S3 for training. Online-only suits pure real-time inference where past feature values are never needed for model training or point-in-time lookups.
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Create a feature group with only an offline store enabled
Why it's wrong here
An offline-only feature group stores records solely in the S3 offline store, so real-time inference cannot retrieve features at low latency; the online store must also be enabled. Offline-only is tempting because historical training needs the offline store, and it would be correct for a batch-only pipeline that never serves live predictions.

Quick reference

AWS S3 Storage Class Comparison

Storage Class	Min Duration	Retrieval	Use Case
S3 Standard	None	Immediate	Frequently accessed data
S3 Standard-IA	30 days	Immediate	Infrequent access, rapid retrieval
S3 One Zone-IA	30 days	Immediate	Non-critical infrequent data
S3 Intelligent-Tiering	None	Immediate–hours	Unknown or changing access patterns
S3 Glacier Instant	90 days	Milliseconds	Archive with instant retrieval
S3 Glacier Flexible	90 days	Minutes–hours	Archive, flexible retrieval
S3 Glacier Deep Archive	180 days	Hours	Long-term compliance archive

Go deeper

MLA-C01 Practice Question: A machine learning team is building a feature…

Why each option matters

Option-by-option breakdown

AWS S3 Storage Class Comparison

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