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AI-300 · domain

Generative AI Optimization

Practise Microsoft Certified: Machine Learning Operations Engineer Associate (AI-300) (AI-300) Generative AI Optimization practice questions — original exam-style scenarios with answer choices, explanations, and analysis of common mistakes.

26 questions8 easy10 medium8 hard

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What this domain covers

What to know about Generative AI Optimization

Generative AI Optimization questions test whether you can apply the concept in context, not just recognise a definition.

How the topic appears in realistic exam-style scenarios.

Which detail in the question changes the correct answer.

How to eliminate plausible but wrong options.

How to connect the question back to the wider exam objective.

Watch out for

Common Generative AI Optimization exam traps

  • Answering from memory before reading the full scenario.
  • Missing a constraint such as cost, availability, security, scope or command context.
  • Choosing a broad answer when the question asks for the most specific fix.
  • Ignoring why the wrong options are tempting.

Question index

All Generative AI Optimization questions (26)

Click any question to see the full explanation, or start a practice session above.

1

Which TWO techniques should you use to improve the accuracy of a RAG pipeline?

Hard
2

Which THREE factors contribute to increased latency in an LLM application?

Medium
3

You want to evaluate your prompt engineering changes quantitatively. Which method is most reliable for comparing two prompt versions?

Medium
4

You are configuring a chat application. What is the benefit of enabling streaming in the Azure OpenAI API?

Easy
5

You are fine-tuning a model on Azure OpenAI and notice the training loss curve is fluctuating significantly. What is the most likely cause?

Hard
6

A user wants to restrict the model's output to valid JSON format. What is the most effective way to ensure this?

Medium
7

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?

Medium
8

You are optimizing a model for a specific domain language. Which fine-tuning approach minimizes cost while maximizing domain adaptation?

Hard
9

Which TWO of the following strategies are commonly used to optimize for cost in Generative AI?

Easy
10

Which THREE parameters directly affect the output structure or style of an LLM response?

Hard
11

Which THREE metrics are critical for monitoring a production Generative AI system?

Easy
12

You have a large set of documents for a RAG system. How should you optimize retrieval speed?

Medium
13

Which parameter in the Azure OpenAI API should be adjusted to make the model's output more deterministic and repeatable?

Easy
14

You are optimizing prompt latency by reducing tokens. Which of the following is the most effective way to reduce input token count for a recurring task?

Hard
15

You are optimizing a long-context application. Which technique is most effective for reducing context window costs in Azure OpenAI?

Hard
16

An application is hitting rate limits on the Azure OpenAI service. Which action is the most standard approach for handling this in production?

Easy
17

Which feature in Azure OpenAI allows you to reserve throughput for a consistent user experience during high demand?

Easy
18

Which TWO methods are best for debugging an LLM pipeline that fails on complex queries?

Medium
19

You notice that your fine-tuned model is 'forgetting' base capabilities after training on a small dataset. What strategy should you use to mitigate this?

Medium
20

Which cost-tracking tool in the Azure portal allows you to view usage by specific Azure OpenAI deployments?

Easy
21

You are debugging a prompt that is performing poorly on edge cases. You decide to use a 'Chain-of-Thought' approach. Why does this improve performance?

Hard
22

Which TWO of the following are effective ways to reduce hallucination?

Easy
23

You notice your model is outputting redundant information. Which parameter specifically targets the penalty for repeating tokens?

Hard
24

Which THREE steps are necessary to successfully fine-tune an Azure OpenAI model?

Medium
25

To optimize costs for an enterprise chatbot, you want to implement token usage monitoring. Which Azure service should you integrate to track token consumption per user?

Medium
26

Which technique is most appropriate for optimizing RAG performance when the vector database returns too much noisy information?

Medium

Frequently asked questions

What does the Generative AI Optimization domain cover on the AI-300 exam?
Generative AI Optimization questions test whether you can apply the concept in context, not just recognise a definition.
How many questions are in this domain?
This page lists all 26 Generative AI Optimization questions in the AI-300 question bank. The actual exam draws from this domain proportionally to its weighting in the official exam blueprint.
What is the best way to practise this domain?
Start with a short focused session (10 questions) to identify gaps, then work through explanations. Repeat with a longer session once the weak areas feel solid.
Can I practise only Generative AI Optimization questions?
Yes — the session launcher on this page filters questions to this domain only. Choose any session length for inline explanations and scoring.
microsoft-ai300 MICROSOFT-AI300 generative ai optimization Practice Questions