Two-stage video recommender
Design a two-stage candidate-generation + rerank recommender for a short-video app with 200M DAU and a 1B-video corpus, where each 'next videos' request must return a diverse, fresh slate in under 120ms p99. Stage one retrieves a few thousand candidates from a billion-item corpus; stage two reranks to a final ~20 with a heavier model. Cover both stages, how candidate generation stays unbiased and diverse, and how the two stages are kept aligned.
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.
Clarify6:00 left
Estimate6:00 planned
Design18:30 planned
Deep dive14:30 planned
Failure10:00 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.