AI-300 Genaiops Infrastructure Practice Question
You are troubleshooting high latency in a RAG-based application. The vector search is fast, but the generation phase is slow. Which component should be scaled?
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
✓
Increase the 'Instances' count for the LLM deployment.
If the generation phase is slow, you likely need to scale the LLM inference endpoint (e.g., increasing instances or provisioned capacity).
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Optimize the vector database index.
Why it's wrong here
The issue is in generation, not the vector retrieval phase.
- ✓
Increase the 'Instances' count for the LLM deployment.
Why this is correct
Scaling the LLM inference instance count increases generation throughput.
- ✗
Reduce the 'temperature' setting.
Why it's wrong here
Temperature impacts response quality, not inference latency.
- ✗
Increase the 'Embedding Model' throughput.
Why it's wrong here
The bottleneck is in generation, not embedding.
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.