Classical ML
Point-in-Time Correctness
Building each training row from only the feature values that existed at that row's timestamp.
Why interviewers ask about it
The structural defence against temporal leakage, and the main reason feature stores exist. Say 'what did we know about entity X at time T?'.
Related terms
Feature Store
A system providing point-in-time correct training-set generation and a low-latency online store serving identical values.
Data Leakage
Any information present at training time that will not be available at prediction time - from the target, from the future, from preprocessing fitted on the full dataset, or from entity overlap across splits.
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