AI-200 Data Management Services And Vector Search Practice Question
You are storing embeddings generated by text-embedding-3-small in Azure Cosmos DB for NoSQL. Which indexing policy property must be explicitly configured to enable vector search queries on your container?
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
✓
Vector embedding policy and vector indexes
To run vector search queries in Azure Cosmos DB for NoSQL, the container's indexing policy must include a dedicated vector embedding path with a datatype of 'vector'.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Vector embedding policy and vector indexes
Why this is correct
Azure Cosmos DB requires configuring a vector embedding policy and defining vector indexes within the indexing policy to enable vector distance functions.
- ✗
Full-text search indexing policy
Why it's wrong here
Full-text search indexes handle keyword tokenization, whereas vector indexes handle numerical array distance computations.
- ✗
Composite indexes containing string fields
Why it's wrong here
Composite indexes optimize multi-property equality and range queries, not vector similarity.
- ✗
Included paths with geospatial indexes
Why it's wrong here
Geospatial indexes are designed for latitude and longitude data, not high-dimensional vector embeddings.
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JA
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
This AI-200 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-200 exam.