AI-200 Data Management Services And Vector Search Practice Question
Which similarity metric measures the cosine of the angle between two vectors, focusing on orientation rather than magnitude, and is commonly used in Azure AI Search?
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
Cosine distance/similarity measures the angle between two vectors and is one of the standard supported metrics in Azure AI Search and Cosmos DB.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
cosine
Why this is correct
Cosine similarity measures the orientation of vectors regardless of their magnitude.
- ✗
manhattan
Why it's wrong here
Manhattan distance is not a standard primary metric for high-dimensional LLM embeddings in Azure AI Search.
- ✗
euclidean
Why it's wrong here
Euclidean distance measures the straight-line distance between two points in Euclidean space.
- ✗
dotProduct
Why it's wrong here
Dot product takes magnitude into account unless vectors are normalized.
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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.