AI-103 Implement Generative AI And Agentic Solutions Practice Question
You are optimizing a RAG pipeline in Azure AI Foundry. The model is hallucinating when retrieving documents from a large corpus. Which strategy should you implement to improve grounding accuracy?
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
✓
Implement a Cross-Encoder re-ranking step
Adding a re-ranking step after the initial search significantly improves the relevance of the context provided to the LLM.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the temperature of the model to 1.0
Why it's wrong here
Increasing temperature increases creativity and hallucination, it does not fix grounding.
- ✗
Use a larger embedding model
Why it's wrong here
While helpful, it doesn't solve the retrieval-to-generation grounding issue as effectively as re-ranking.
- ✓
Implement a Cross-Encoder re-ranking step
Why this is correct
Re-ranking ensures only the most contextually relevant chunks are sent to the LLM.
- ✗
Increase the chunk size to include the entire document
Why it's wrong here
Larger chunks can introduce noise and exceed token limits, potentially reducing accuracy.
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.