Design a large-scale text classification system (e.g. news categorization or topic tagging).
Tagging articles at scale is bread-and-butter ML, but the naive version fails three ways: it is multi-label, the taxonomy is hierarchical and keeps growing, and rare classes hide behind aggregate accuracy. What matters is the model choice plus how you handle all three.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Tagging articles at scale is bread-and-butter ML, but the naive version fails three ways: it is multi-label, the taxonomy is hierarchical and keeps growing, and rare classes hide behind aggregate accuracy. What matters is the model choice plus how you handle all three.
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.
