hardMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A company uses Amazon SageMaker Feature Store to…
A company uses Amazon SageMaker Feature Store to store features for a real-time recommendation model. The feature data is updated continuously, and the model must use the most recent feature values for each user at inference time. Which type of Feature Store should the company use for serving features to the model?
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
✓
Online store
Online store provides low-latency access to the latest feature values for real-time inference. Offline store is for batch training and analytics. Point-in-time queries are for historical retrieval, not real-time serving.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Offline store
Why it's wrong here
Offline store is for batch access, not low-latency real-time inference.
- ✗
Point-in-time queries
Why it's wrong here
Point-in-time queries are used for historical feature retrieval, not real-time serving.
- ✓
Online store
Why this is correct
An Amazon SageMaker Feature Store online store serves features with low-latency access for real-time inference, satisfying the requirement that the recommendation model must use the most recent feature values per user at inference time. Unlike the offline store, which is optimised for batch retrieval and historical analysis, the online store provides a synchronised, low-latency key-value lookup for continuously updated feature data.
- ✗
Both online and offline store
Why it's wrong here
Only the online store is needed for real-time inference; using both adds unnecessary complexity.
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