Photo near-duplicate detection
Design the near-duplicate detection + clustering system for a consumer photo-backup product that has 100B photos and wants to (a) avoid re-storing the same photo a user uploads twice from different devices, and (b) group visually near-identical shots (burst frames, the same scene re-shared/recompressed) so the UI can show one representative and the user can free space. Exact-hash dedup misses re-encoded/resized copies; you need perceptual matching at planet scale without an O(n^2) all-pairs comparison.
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.
Clarify4:30 left
Estimate4:30 planned
Design13:30 planned
Deep dive10:30 planned
Failure7:00 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.