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Database / Vector database interview questions

How does normalization affect dot-product and cosine search?

L2 normalization aligns dot product behavior with cosine similarity for many setups. Consistent normalization between indexing and querying is critical for predictable relevance.

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What is a vector database, and why is it used in modern AI systems? How does vector similarity search differ from keyword search? What are embeddings in the context of vector databases? Which distance metrics are commonly used in vector databases? When should you choose cosine similarity over Euclidean distance? What is Approximate Nearest Neighbor (ANN), and why is it important? How does HNSW indexing work at a high level? What are IVF and PQ in vector indexing? How do you evaluate recall and latency in vector search systems? What does top-k mean in vector retrieval? How does metadata filtering work with vector search? What is hybrid search in vector databases? How do rerankers improve vector retrieval pipelines? What is the role of vector databases in RAG architectures? How do chunking strategies affect vector database retrieval quality? Why is embedding model choice critical for vector database performance? How should you handle embedding model upgrades in production? What are the trade-offs between managed and self-hosted vector databases? How do you design a schema for documents and vectors? What is upsert behavior in vector databases? How do deletions and tombstones impact vector index maintenance? How do you prevent duplicate vectors in ingestion pipelines? What are common causes of poor relevance in vector search? How can query rewriting improve vector search outcomes? What is multi-vector representation for a single document? How do sparse and dense vectors complement each other? What is vector quantization, and when is it used? How do you choose vector dimensionality for an application? How does normalization affect dot-product and cosine search? What operational metrics should you monitor in vector databases? How do you benchmark vector databases fairly? What is multi-tenancy in vector databases, and how is it implemented? How do access control and authorization apply to vector retrieval? How do you handle fresh content and eventual consistency in vector systems? What backup and disaster recovery considerations exist for vector databases? How do vector databases support recommendation systems? What are common cost drivers in vector database deployments? How do caching layers help vector search workloads? What is the difference between online and offline indexing strategies? How do you test quality regressions after index parameter changes? What role do namespaces or collections play in vector databases? How can you reduce hallucinations using better vector retrieval? How do you secure sensitive data in vector database pipelines? How should teams handle multilingual vector search? What are best practices for productionizing vector database systems?
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