AI-200 Data Management Services And Vector Search Practice Question
You are developing an enterprise retrieval-augmented generation (RAG) application using Azure AI Search. You need to configure a vector index to store dense vector embeddings alongside traditional text fields. Which property within the Azure AI Search index definition must be explicitly configured with a vector search profile to enable HNSW (Hierarchical Navigable Small World) algorithm indexing?
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
✓
vectorSearchProfile
The vectorSearch configuration block inside an Azure AI Search index definition requires a profiles array where algorithms and vectorizer settings are tied together. Each searchable vector field then references the vector search profile via the vectorSearchProfile property.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
vectorIndexKind
Why it's wrong here
vectorIndexKind is not a valid property name in the Azure AI Search REST API index schema.
- ✗
compressionMethod
Why it's wrong here
compressionMethod is used for scalar quantization settings, not for assigning the base HNSW algorithm profile.
- ✓
vectorSearchProfile
Why this is correct
The vectorSearchProfile property links a vector field to a defined vector search algorithm configuration and optional vectorizer.
- ✗
similarityFunction
Why it's wrong here
similarityFunction is used within scoring profiles for traditional text relevance scoring, not for assigning the HNSW algorithm to vector fields.
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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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