Compare batch, stochastic, and mini-batch gradient descent (and momentum).
Everyone says 'gradient descent,' but the batch-size decision and momentum are the actual interview content. What earns credit is the noise-vs-cost tradeoff across batch/SGD/mini-batch and why momentum pays off.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Everyone says 'gradient descent,' but the batch-size decision and momentum are the actual interview content. What earns credit is the noise-vs-cost tradeoff across batch/SGD/mini-batch and why momentum pays off.
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.
