Author(s): Shahidullah Kawsar

Originally published on Towards AI.

AI Engineer Interview Preparation

Let’s check your basic knowledge of Vector Databases. Here are 10 Q&A for your next interview.

The rest of the article continues with scenario-based multiple-choice questions and explanations that cover key concepts in vector databases and semantic search: why linear scans are too slow for large collections, how embedding similarity differs from relational equality search, what tradeoffs approximate nearest neighbor indexes make (faster retrieval at the cost of perfect recall), how HNSW structures graph layers for efficient search, and what capabilities vector platforms must provide in production (attribute/metadata filtering, multi-tenant permissions, and access control). It also addresses when integrating vector search into an existing relational database is reasonable, why compliance or high-stakes workflows often require higher recall and reranking/exact verification, how eventual consistency between the primary system and the vector index can cause deleted content to remain searchable, and how HNSW query traversal proceeds from sparse entry layers down to denser layers for final candidate selection.

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Published via Towards AI