AI-200 Data Management Services And Vector Search Practice Question
You are configuring a Vector Index in Azure AI Search. You need to ensure that the vector similarity search is performed using the Cosine distance metric. Where is this configuration specified?
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
✓
In the 'vectorSearch' section of the index definition.
The distance metric (e.g., cosine, euclidean, dotProduct) is specified within the 'vectorSearch' profile configuration in the index definition.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
In the indexer definition.
Why it's wrong here
The indexer is responsible for data ingestion, not the index configuration for search metrics.
- ✓
In the 'vectorSearch' section of the index definition.
Why this is correct
The vector search profile within the index schema defines the distance metric to be used.
- ✗
In the search query payload.
Why it's wrong here
The metric must be defined at index creation; it cannot be changed dynamically per query.
- ✗
In the field definitions within the index.
Why it's wrong here
Field definitions define the schema, but the search profile defines the algorithm and metric.
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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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