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MLA-C01 Practice Question: An ML team is using Amazon SageMaker Feature…
An ML team is using Amazon SageMaker Feature Store to serve features for both real-time inference and batch training. They need to ensure that training data uses feature values as they were at the time of each event. Which type of query should they use?
⚠ Common exam trap
MLA-C01 often tests the confusion between online-store 'latest value' retrieval and offline-store point-in-time retrieval — candidates must recognize that training requires historical, time-correct values, not the freshest ones.
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
✓
Point-in-time query from the offline store
SageMaker Feature Store's offline store supports point-in-time queries, which return feature values as they existed at a specified event timestamp for each record. This prevents 'feature leakage' — using future feature values that were not available when the event occurred — which would otherwise inflate training accuracy and break production parity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Full table scan of the offline store
Why it's wrong here
A full table scan returns every version of each record without selecting the value current at each event's timestamp, so it does not provide point-in-time correctness. It is tempting because the offline store does hold the timestamped history, and would be correct when retrieving all versions for auditing rather than training.
- ✗
Join query across both stores
Why it's wrong here
A join query merges records from the online and offline stores, so it returns current values rather than the historical ones recorded at each event time. It suits combining feature groups for training datasets, but point-in-time correctness requires an as-of query against the offline store.
- ✗
Latest record query from the online store
Why it's wrong here
The online store retains only the latest feature value per record, so it cannot reconstruct point-in-time values for training. It is tempting because the online store is the correct source for low-latency real-time inference, but batch training requires the offline store's timestamped history.
- ✓
Point-in-time query from the offline store
Why this is correct
Point-in-time queries from the offline store retrieve feature values as they existed at each event's timestamp, preventing label leakage from later updates. This directly satisfies the requirement that training data reflect historical feature states, since the offline store retains the full time-ordered history that the online store does not.
Quick reference
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Written and reviewed by Johnson Ajibi, MSc IT Security
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.