AI-200 Data Management Services And Vector Search Practice Question
When storing vector embeddings in Azure Cosmos DB for MongoDB (vCore), which distance metric is natively supported when creating a vector search index?
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
✓
Cosine distance (cos)
Azure Cosmos DB for MongoDB (vCore) supports cosine distance, inner product, and Euclidean distance for vector search indexing.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Levenshtein distance
Why it's wrong here
Levenshtein distance measures edit distance between strings, not vector similarity.
- ✗
Jaccard distance
Why it's wrong here
Jaccard distance is used for sets, not continuous vector embeddings.
- ✗
Hamming distance
Why it's wrong here
Hamming distance is used for binary string comparisons.
- ✓
Cosine distance (cos)
Why this is correct
Cosine distance is one of the core supported distance metrics for vector indexes in Azure Cosmos DB for MongoDB (vCore).
About these practice questions
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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.