AI-200 Data Management Services And Vector Search Practice Question
You are configuring a hybrid search in Azure AI Search. You want to combine results from a vector search and a keyword-based search. Which feature is specifically designed to normalize scores from these different retrieval methods into a single ranked list?
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
✓
Reciprocal Rank Fusion (RRF)
Reciprocal Rank Fusion (RRF) is the standard algorithm used in Azure AI Search to combine scores from different search mechanisms into a single, cohesive ranking.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
BM25 Scoring
Why it's wrong here
BM25 is a keyword relevance algorithm, not a normalization tool for hybrid vector-keyword scoring.
- ✗
Semantic Ranker
Why it's wrong here
The Semantic Ranker re-ranks results based on relevance; it is not the mechanism for normalizing hybrid search scores.
- ✗
Vector Normalization
Why it's wrong here
Vector normalization applies to the embedding vectors themselves, not the final search result ranks.
- ✓
Reciprocal Rank Fusion (RRF)
Why this is correct
RRF normalizes disparate scores from multiple search techniques to provide a better combined ranking.
About these practice questions
Courseiva writes every AI-200 question from scratch — 507 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
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.