Design Patterns Interview Questions | Qdrant | NextRound

1Why is it important to decide on vector dimensionality and distance metric carefully before going to production, rather than treating them as easy-to-change settings?Intermediate

2What factors should drive the decision to split data across multiple collections versus one collection with a discriminator payload field?Advanced

3What is a sensible payload schema design for a document search system that needs to support filtering by date range, category, and access permissions?Advanced

4When does it make sense to chunk a long document into multiple points rather than storing one embedding per document?Intermediate

5Why should you avoid indexing every payload field 'just in case,' and how would you decide which fields actually need an index?Intermediate

6What's a good practice for storing large binary blobs (e.g., raw images) associated with a point, given payloads are meant for structured metadata?Intermediate

7How would you structure payload data to support both 'show me items in category X' and 'show me items NOT in category X' filters efficiently?Intermediate

8What retry and backoff strategy would you implement in a client that writes to Qdrant, and why does naive immediate retry risk making problems worse?Advanced

9Why should embedding generation and Qdrant upserts typically be decoupled via a queue rather than done synchronously in a request handler?Advanced

10What versioning strategy would you use so that switching to a new embedding model doesn't silently corrupt search quality for existing data?Advanced