AI-200 Data Management Services And Vector Search Practice Question
You are developing a retrieval-augmented generation (RAG) solution using Azure AI Search. You need to configure a vector index to store 1536-dimensional embeddings generated by text-embedding-ada-002. Which parameter must you configure in the vector profile's algorithm configuration to use HNSW as the underlying approximate nearest neighbor algorithm?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
algorithm.name set to hnsw
To configure HNSW in Azure AI Search, you must define a vectorizer and an algorithm configuration specifying algorithm parameters such as m, efConstruction, and metric under the vectorSearch property of the index.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
algorithm.name set to exhaustiveKnn
Why it's wrong here
exhaustiveKnn performs an exact search rather than approximate nearest neighbor search via HNSW.
- ✗
vectorSearch.compression set to scalarQuantization
Why it's wrong here
Scalar quantization is an optional compression technique, not the core algorithm selection parameter.
- ✓
algorithm.name set to hnsw
Why this is correct
Setting algorithm.name to hnsw instructs Azure AI Search to use the Hierarchical Navigable Small World graph algorithm for nearest neighbor search.
- ✗
vectorSearch.algorithm.type set to faiss
Why it's wrong here
FAISS is not a native selectable configuration name in Azure AI Search; HNSW and exhaustiveKnn are the supported algorithm types.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed August 2026 · checked against the official Microsoft exam blueprint
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