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Browse 3 real-world technical and behavioral interview questions about Point in time. Review scenarios, edge cases, and architectural best practices.
Train-serve skew is a mismatch between features used in training and features produced at serving time. Prevent it with shared transformations, point-in-time joins, feature logging, schema checks and monitoring of served feature distributions. It also connects feature store to the point an interviewer is testing.
Establish that both are talking about the same instrument, then about the same series. Adjusted histories are rewritten by every subsequent corporate action, tickers get reused, official closing prices are not last trades, and the currency conversion is a second series with all the same problems.
Target leakage happens when a feature contains information that would not be available at prediction time. Catch it by auditing feature creation times, window boundaries, post-label columns and suspiciously strong validation performance. Use this data leakage answer to show the decision, trade-off, and evidence rather than a memorised definition.