AI-200 Data Management Services And Vector Search Practice Question
You are configuring vector search options in Azure Cosmos DB for NoSQL. Which TWO distance metrics are supported when defining a vector embedding policy? (Choose two)
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
✓
euclidean
Azure Cosmos DB for NoSQL supports cosine, dotproduct, and euclidean distance metrics for vector similarity search.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
hamming
Why it's wrong here
Hamming distance is not supported for vector similarity in Cosmos DB.
- ✗
manhattan
Why it's wrong here
Manhattan distance is not a supported distance metric in Cosmos DB vector search.
- ✗
jaccard
Why it's wrong here
Jaccard distance is used for binary or sets data, not continuous embeddings.
- ✓
euclidean
Why this is correct
Euclidean distance is supported in Azure Cosmos DB vector search.
- ✓
cosine
Why this is correct
Cosine similarity is supported in Azure Cosmos DB vector search.
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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.