AI-300 Generative AI Optimization Practice Question
You are optimizing a long-context application. Which technique is most effective for reducing context window costs in Azure OpenAI?
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
✓
Summarizing conversation history before passing to the next prompt.
Summarizing previous turns in a conversation history reduces the number of tokens sent in each request, lowering costs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Using a smaller batch size.
Why it's wrong here
Batch size does not impact context window token cost per request.
- ✓
Summarizing conversation history before passing to the next prompt.
Why this is correct
Summarization compresses token counts significantly compared to passing full history.
- ✗
Disabling streaming responses.
Why it's wrong here
Streaming affects UX, not total token costs.
- ✗
Increasing the frequency penalty.
Why it's wrong here
Penalties do not affect token count.
About these practice questions
This AI-300 question is part of Courseiva's 204-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam 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.