RAG and Vector Databases interview questions

Retrieval pipelines, embeddings, and grounding.

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Not started41.Compare HNSW, IVF and flat indexes. How do you pick one, and what does recall@k cost in latency?RAG and Vector Databases · Conceptual · 12 minAsked %CompaniesRAG and Vector DatabasesConceptual12 minPro featurePro featureHard
Not started42.What is the role of metadata filtering in RAG systems?RAG and Vector Databases · Conceptual · 9 minAsked %CompaniesRAG and Vector DatabasesConceptual9 minPro featurePro featureMedium
Not started43.Build vector similarity search from scratch: cosine, dot product and Euclidean distanceRAG and Vector Databases · Practical exercise · 18 minAsked %CompaniesRAG and Vector DatabasesPractical exercise18 minPro featurePro featureMedium
Not started44.What is Retrieval-Augmented Generation (RAG), and why is it important?RAG and Vector Databases · Conceptual · 8 minAsked %CompaniesRAG and Vector DatabasesConceptual8 minPro featurePro featureEasy
Not started45.How do you handle structured data (tables, SQL databases) in a RAG pipeline?RAG and Vector Databases · Practical exercise · 11 minAsked %CompaniesRAG and Vector DatabasesPractical exercise11 minPro featurePro featureMedium
Not started46.How do you index and query multi-tenant data in a vector database?RAG and Vector Databases · System design · 12 minAsked %CompaniesRAG and Vector DatabasesSystem design12 minPro featurePro featureHard
Not started47.Long context windows keep getting cheaper. When should you use RAG vs putting everything in the context window?RAG and Vector Databases · Conceptual · 11 minAsked %CompaniesRAG and Vector DatabasesConceptual11 minPro featurePro featureHard