Implement a dropout layer (train and inference) from scratch.
Dropout takes five lines, yet most candidates fumble two of them: turning it off at inference and rescaling the survivors so the expected activation holds constant. Inverted dropout is the convention they want to see.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Dropout takes five lines, yet most candidates fumble two of them: turning it off at inference and rescaling the survivors so the expected activation holds constant. Inverted dropout is the convention they want to see.
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.
