What is dropout, and how does it regularize a neural network?
Dropout is the classic neural-net regularizer, but what matters is whether you can explain why zeroing activations forces redundancy, and the train-vs-inference scaling bug that trips most candidates. Here is the answer.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Dropout is the classic neural-net regularizer, but what matters is whether you can explain why zeroing activations forces redundancy, and the train-vs-inference scaling bug that trips most candidates. Here is the answer.
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.
