What cross-validation strategy do you use, and how do you avoid leakage in CV?
Cross-validation yields a reliable performance estimate, but the wrong scheme leaks data and misleads. What matters is matching the CV scheme to the data (stratified, grouped, time-series) and fitting preprocessing inside the fold.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Cross-validation yields a reliable performance estimate, but the wrong scheme leaks data and misleads. What matters is matching the CV scheme to the data (stratified, grouped, time-series) and fitting preprocessing inside the fold.
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.
