AI-300 Generative AI Optimization Practice Question
Which TWO techniques should you use to improve the accuracy of a RAG pipeline?
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
✓
Upgrading to a more advanced embedding model.
Chunking strategy and better embedding models directly impact the quality of retrieved data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reducing the number of documents in the vector store.
Why it's wrong here
This reduces knowledge, not accuracy.
- ✓
Upgrading to a more advanced embedding model.
Why this is correct
Higher quality embeddings lead to better semantic relevance.
- ✗
Increasing the temperature to 2.0.
Why it's wrong here
Too much randomness destroys accuracy.
- ✓
Optimizing document chunking strategy.
Why this is correct
Proper chunking ensures meaningful context is retrieved.
- ✗
Using only one single long chunk for all documents.
Why it's wrong here
This loses retrieval precision.
About these practice questions
One of 204 original AI-300 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. 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.