Generate ML labels in SQL: did each user churn (no activity in the next 30 days)?
Defining the label is half the modeling problem, and the SQL conceals two leakage traps: reaching into the future for features, and a label window not yet fully observed. Here is the correct query.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Defining the label is half the modeling problem, and the SQL conceals two leakage traps: reaching into the future for features, and a label window not yet fully observed. Here is the correct query.
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.
