Two of your features are highly correlated. Does it hurt the model, and what do you do about it?
The textbook reflex ('drop one') is usually the wrong instinct, and whether collinearity matters at all depends on your model and what you want from it. This is the answer that tells rote apart from real understanding.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
The textbook reflex ('drop one') is usually the wrong instinct, and whether collinearity matters at all depends on your model and what you want from it. This is the answer that tells rote apart from real understanding.
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.
