Contrast L1 and L2 regularization. Why does L1 produce sparse weights?
A near-universal ML fundamentals question. Anyone can say 'L1 is lasso, L2 is ridge'; the signal is the gradient-and-geometry reason L1 drives weights to exactly zero and when you'd pick each. Here is that answer.
Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.
A near-universal ML fundamentals question. Anyone can say 'L1 is lasso, L2 is ridge'; the signal is the gradient-and-geometry reason L1 drives weights to exactly zero and when you'd pick each. Here is that 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.