AI-300 Generative AI Optimization Practice Question
Which technique is most appropriate for optimizing RAG performance when the vector database returns too much noisy information?
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
✓
Implementing a re-ranking stage.
Re-ranking filters top retrieved results to ensure high-quality context is passed 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.
- ✗
Changing the embedding model to a smaller one.
Why it's wrong here
Smaller models generally have lower retrieval quality.
- ✓
Implementing a re-ranking stage.
Why this is correct
Re-ranking improves precision of context retrieval.
- ✗
Increasing the number of chunks retrieved.
Why it's wrong here
This adds more noise.
- ✗
Reducing the temperature to 0.
Why it's wrong here
This affects generation, not context noise.
About these practice questions
Courseiva writes every AI-300 question from scratch — 204 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-300 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-300 exam.