Build an ML training set in SQL with point-in-time-correct feature joins (no future leakage).
The most frequent way SQL leaks the future into a training set is a sloppy join to a feature table. The fix is point-in-time correctness, implemented as an as-of join. Here is how to write it.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
The most frequent way SQL leaks the future into a training set is a sloppy join to a feature table. The fix is point-in-time correctness, implemented as an as-of join. Here is how to write it.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
