AI0-001 AI Infrastructure and Technologies Practice Question
An AI platform team is building a feature store that feeds both offline training jobs and an online model that must return features within a few milliseconds. They are concerned that a feature computed one way during training could be computed differently at serving time. Which design choice best prevents this training-serving skew?
⚠ Common exam trap
The trap here is assuming that logging production data and retraining closes the gap, when skew comes from divergent feature logic rather than from differing input distributions.
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
✓
Define each feature once in a shared transformation definition that is executed by both the batch and online paths.
Training-serving skew arises when the same feature is computed by different logic in the offline and online environments. A shared transformation definition executed by both engines makes the computation identical by construction, and the online path simply materializes the result into a low-latency store, satisfying the millisecond requirement without duplicating logic.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Precompute all features nightly and have the online model read them from the same batch tables used for training.
Why it's wrong here
Nightly batch tables cannot reflect events that occur between runs, so the online model would serve stale values that do not match what the training data implied at prediction time. Reading directly from analytical tables also introduces query latency that conflicts with the few-millisecond serving requirement, and it couples online availability to batch job completion.
- ✗
Log raw request payloads during serving and retrain the model on that log so training data matches production inputs.
Why it's wrong here
Logging raw payloads and retraining addresses input distribution drift, not the computational mismatch between how a feature is derived offline and online. The same raw input can still yield different feature values if the two code paths differ, so this approach leaves the skew mechanism intact while adding considerable retraining overhead.
- ✓
Define each feature once in a shared transformation definition that is executed by both the batch and online paths.
Why this is correct
A single transformation definition executed by both the offline and online engines guarantees that the same logic produces training values and serving values, which removes the divergence that causes skew. The online engine materializes the result in a low-latency store, so millisecond serving requirements are met without duplicating the feature logic.
- ✗
Compute features with separate code paths for batch training and online serving, and reconcile differences in periodic audits.
Why it's wrong here
Maintaining two independent implementations is the classic source of training-serving skew, because any change to one path can silently diverge from the other. Periodic audits detect drift only after it has affected production predictions, so this design accepts the very risk the team is trying to eliminate rather than preventing it structurally.
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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 CompTIA exam blueprint
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