A fraud team has computed a customer's rolling 30-day transaction count in a batch job and now needs the identical value served at low latency during online inference, while guaranteeing that training data drawn from history matches what the model will see in production. Their platform offers the Agent Platform Feature Store. Which capability of the Feature Store most directly prevents training-serving skew for this feature?
- AA managed offline store for point-in-time training reads and an online store serving the same feature definition at low latency. Correct
- BAutomatic hyperparameter tuning that retrains the fraud model whenever the rolling-count feature distribution drifts beyond a threshold.
- CColumn-level encryption that masks the transaction count so that training and serving both read an obfuscated value.
- DA built-in cache that stores recent model predictions so repeated inference requests reuse the previous scored output.
Why A is correct: The Feature Store pairs an offline store that supports point-in-time correct training lookups with an online store serving the same registered feature, so both paths read one definition and skew is avoided by construction.
Why B is wrong: Drift-triggered retraining is a real lifecycle practice and sounds relevant to changing features, but it addresses model staleness rather than the Feature Store's job of serving consistent feature values across training and inference.
Why C is wrong: Encryption and masking protect sensitive fields and could plausibly be confused with consistency controls, but obfuscating a value does nothing to keep training and serving reads aligned, which is the skew problem here.
Why D is wrong: Prediction caching can cut latency and seems to help serving, but it stores outputs rather than feature inputs and never touches how training reads historical features, so it cannot prevent skew.