向量数据库与嵌入

题集来源:AMIT SHEKHAR(amitshekhariitbhu)收录项目:ai-engineering-interview-questions查看原题集

题目目录

26 道原题
  1. What is embedding dimensionality, and how does it affect performance and cost?

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  2. How do you handle embedding drift when the embedding model is updated?

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  3. What are multi-modal embeddings, and how are they generated?

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  4. How do you index and query multi-tenant data in a vector database?

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  5. What is quantization of embeddings, and how does it reduce storage costs?

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  6. How do you benchmark and evaluate embedding model quality?

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  7. What is the role of metadata in vector databases?

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  8. How do you handle large-scale vector search with billions of vectors?

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  9. What is hybrid search (combining keyword search with vector search)?

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  10. How do you fine-tune an embedding model for a specific domain?

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