ETA prediction service
Design an ETA prediction service that powers a navigation app serving 1M route requests/minute. ETAs must reflect live traffic that changes minute-to-minute, must be returned in <100ms p99 for a route that may span hundreds of road segments, and must stay accurate across rush hour, incidents, and weather. You have a fleet of probe vehicles feeding live speeds. Describe how you ingest live traffic onto the road graph, compute the ETA, serve it at low latency, and reconcile the physics-based segment model with a learned correction.
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.