Recommendation feed generation and ranking
Design the full candidate-generation → re-ranking pipeline for a recommendation feed over a corpus of 1B items for 300M users, returning a feed of 50 items in under 150ms. You can't score 1B items per request, so describe the funnel: how each stage narrows the corpus, what model/index each stage uses, and how you keep the stages from fighting each other (e.g. the re-ranker can only re-rank what generation surfaced). Where does business logic / diversity / freshness get injected?
What a strong answer looks like
Clarify scale and constraints first. Propose a clean component breakdown, then go deep on the hard parts (data model, bottlenecks, consistency, failure modes) and name the trade-offs you are making.
Clarify5:30 left
Estimate5:30 planned
Design16:30 planned
Deep dive13:30 planned
Failure9:00 planned
Which questions mattered is sealed until you submit. Telling you now would just be handing over the edge cases.
Run or narrate your approach, then ask the coach.