AI-103 Implement Generative AI And Agentic Solutions Practice Question
You are developing an Azure AI Search index for a RAG application. You need to ensure that the search results include semantic relevance scores to improve the quality of the model's grounding. Which feature must you enable?
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
✓
Enable Semantic Ranker on the search service
Semantic ranker is required to provide semantic relevance scores (semantic search) to rerank results.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Custom Analyzers
Why it's wrong here
Custom analyzers improve keyword matching, not semantic relevance.
- ✗
Enable Knowledge Store
Why it's wrong here
Knowledge store is for projecting data into Azure Storage, not for search relevancy.
- ✓
Enable Semantic Ranker on the search service
Why this is correct
Semantic ranker is required for semantic reranking.
- ✗
Enable Hybrid Search with vectorization
Why it's wrong here
Hybrid search combines vector and keyword search but does not inherently provide the reranking capability provided by the semantic ranker.
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-103 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-103 exam.