Feature store online and offline
Design a feature store serving an organization with hundreds of models, where the same feature definitions must produce training data offline (point-in-time correct over years of history) and low-latency lookups online (sub-10ms p99 at 200k QPS). Some features are batch (daily aggregates), some streaming (last-5-minute counts), and some computed on request. Cover the storage layers, how you guarantee online/offline consistency, and how teams register and discover features.
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.