AI-300 Generative AI Optimization Practice Question
Your Azure OpenAI deployment is experiencing high latency during peak hours. You observe that input tokens are consistently high. Which strategy is most effective for reducing latency while maintaining quality?
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 prompt caching for static instructions.
Prompt caching and reduction of redundant input tokens are primary methods for reducing latency in high-traffic deployments.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Implement prompt caching for static instructions.
Why this is correct
Caching static portions of the prompt reduces the computation required for input token processing.
- ✗
Increase the top_p parameter.
Why it's wrong here
Top_p settings do not impact latency significantly.
- ✗
Enable dynamic scaling in Azure App Service.
Why it's wrong here
Scaling the app service does not impact the model's token processing latency.
- ✗
Decrease the number of parallel requests.
Why it's wrong here
This reduces throughput, not individual request latency.
- ✗
Use a higher model version (e.g., o1-preview).
Why it's wrong here
Larger models usually increase latency.
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-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.