Edge Cases Interview Questions | Qdrant | NextRound

1How would you approach re-embedding a multi-billion-point production collection with a new embedding model with zero search downtime?Advanced

2When designing a retrieval system that combines dense, sparse, and multivector reranking at extreme scale, what's the single biggest cost driver you'd optimize first, and why?Advanced

3If you were asked to design Qdrant's filtered-HNSW search from scratch, what core problem would you need to solve, and what naive approach would you reject first?Advanced

4What are the limits of a purely payload-filter-based multitenancy model, and at what point would you need to introduce dedicated shards or collections per tenant instead?Advanced

5Evaluate this claim: 'Cosine similarity and normalized dot product always produce identical rankings.' What subtlety do candidates often miss here?Advanced

6Many candidates assume increasing ef at query time always improves recall with only a linear latency cost. What's misleading about that assumption?Advanced

7Why is 'just add more RAM' not always a valid answer to a Qdrant performance question in a system design interview?Advanced

8A candidate claims that quantization always speeds up search. Under what conditions might quantization with rescoring actually be slower than searching un-quantized vectors?Advanced

9Why can two identical-looking filter queries - one using an indexed field, one using an equivalent but unindexed field - have wildly different performance, even though they return the same results?Advanced

10At billion-point scale, how would your indexing and sharding strategy differ from a design that works fine at ten million points?Advanced

11How would you architect a system to gracefully degrade - rather than fail outright - when a burst of traffic exceeds provisioned Qdrant capacity?Advanced