1
Navigation
© 2026 AIML MCQ. Free forever.
🗄️
Vector Databases MCQ
10 Conceptual Questions with In-Depth Technical Explanations
Understand HNSW graphs, inverted file index (IVF), cosine similarity, approximate nearest neighbor (ANN), and vector indexing.
Vector Databases Questions
Select an answer to reveal detailed technical explanation and formulas.
2
Why is exact k-Nearest Neighbors (k-NN) brute-force search impractical for production search across 10 million vector embeddings?
3
What is the HNSW (Hierarchical Navigable Small World, Malkov & Yashunin, 2018) indexing algorithm?
4
What is the Inverted File Index (IVF) algorithm in vector search (e.g., in FAISS)?
5
What is Product Quantization (PQ, Jégou et al., 2011) in vector database compression?
6
What is 'Metadata Filtering' in vector databases, and what is the difference between Pre-filtering and Post-filtering?
7
What similarity distance metrics are standard across vector databases?
8
What is FAISS (Facebook AI Similarity Search, Johnson et al., 2017)?
9
What is 'pgvector' in the context of relational databases?
10
What happens if you search a vector database using an embedding model different from the one used to index the stored documents?
Explore More Generative AI & Large Language Models Topics
Continue practicing related interview questions.
✨🤖💡✍️📚🎛️
Generative AI MCQ
10 Questions • Generative AI & Large Language Models
Large Language Models (LLM) MCQ
10 Questions • Generative AI & Large Language Models
GPT Models MCQ
10 Questions • Generative AI & Large Language Models
Prompt Engineering MCQ
10 Questions • Generative AI & Large Language Models
Retrieval Augmented Generation (RAG) MCQ
10 Questions • Generative AI & Large Language Models
Fine-Tuning LLM MCQ
10 Questions • Generative AI & Large Language Models
About Vector Databases Interview Questions & Quiz
These Vector Databases multiple choice questions are curated to prepare engineers, researchers, and data science students for top technical interviews, machine learning certifications, and university exams.
Every MCQ incorporates core mathematical formulas, architectural tradeoffs, and practical algorithmic considerations. Practicing these questions guarantees a solid foundational and conceptual mastery of Vector Databases.