Vector search with metadata filter
Design a vector-search service backing a semantic product/document retrieval system: 200M embeddings (768-dim), 10k QPS of top-50 nearest-neighbor queries with a 50ms p99 budget, frequent inserts/deletes as the corpus changes, and a requirement to filter by structured metadata (category, language, in-stock) at query time. Cover the index choice, sharding, how you handle updates and metadata filtering, and the recall/latency trade-off.
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.
Clarify5:00 left
Estimate5:00 planned
Design15:00 planned
Deep dive12:00 planned
Failure8:00 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.