AI-200 Data Management Services And Vector Search Practice Question
You are building a hybrid search solution using Azure AI Search that combines BM25 full-text search with vector search. What is the name of the feature that intelligently combines and normalizes scores from different retrieval systems before presenting the final top results?
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)
Azure AI Search uses Reciprocal Rank Fusion (RRF) as part of semantic ranking and hybrid search score combination to merge disparate result lists.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Min-Max Score Scaling
Why it's wrong here
Min-max scaling is not the primary mechanism used by Azure AI Search to merge hybrid search results.
- ✗
Cosine Distance Normalization
Why it's wrong here
Cosine distance is a similarity metric, not a score combination and normalization algorithm like RRF.
- ✗
Cross-Encoder Re-ranking
Why it's wrong here
While semantic ranker uses cross-encoders, the general score merging technique for hybrid search is RRF.
- ✓
Reciprocal Rank Fusion (RRF)
Why this is correct
RRF is the ranking algorithm used in Azure AI Search to combine scores from vector queries and keyword text queries.
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.