Core Concepts Interview Questions | Qdrant | NextRound

1How should you choose a distance metric when creating a Qdrant collection?Intermediate

2What is approximate nearest-neighbor search, and why is it preferred over brute-force k-NN at scale?Intermediate

3What is Qdrant, and how does it compare with a general-purpose database that provides vector search?Beginner

4What are the three core building blocks of Qdrant's data model?Beginner

5What is a payload in Qdrant, and why is it important in production applications?Beginner

6What are named vectors in Qdrant, and when would one point need multiple vectors?Intermediate

7Can two points in the same Qdrant collection have vectors of different dimensionality?Beginner

8What determines a Qdrant point ID, and what constraints apply to point IDs?Beginner

9What distance metrics does Qdrant support, and what does each one measure?Intermediate

10Why can cosine similarity and dot product rank unnormalized embeddings differently?Intermediate

11What is a vector embedding, and why can semantically similar data be close together in vector space?Beginner

12What is the performance benefit of normalization when using cosine distance in Qdrant?Intermediate

13Why use Qdrant instead of a traditional relational or key-value database for vector similarity search?Intermediate