- A
Temperature
Why wrong: Temperature controls the randomness of token selection. Higher values increase creativity but do not specifically encourage the model to introduce new topics or avoid repeating themes.
- B
Top_p (nucleus sampling)
Why wrong: Top_p limits token selection to the smallest set of tokens whose cumulative probability exceeds a threshold. It influences diversity but does not directly penalize the repetition of topics.
- C
Frequency penalty
Why wrong: Frequency penalty penalizes tokens that have already been used, reducing word-level repetition. It does not specifically discourage the model from returning to the same broader topic.
- D
Presence penalty
Presence penalty penalizes tokens that have already appeared in the generated output, which discourages the model from repeating the same ideas or discussing the same topics repeatedly, thereby encouraging new content.
Quick Answer
The answer is to increase the presence penalty parameter. This parameter directly penalizes tokens that have already appeared in the generated text, reducing the model’s likelihood of repeating the same topics and encouraging it to introduce new subject matter. In the context of the Azure OpenAI Service, this is a core sampling parameter that controls output diversity, distinct from frequency penalty which reduces repetition of specific phrases. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of how to fine-tune generative AI behavior for specific use cases like chatbots. A common trap is confusing presence penalty with temperature or top-p, which control randomness rather than repetition. For a quick memory tip, think of “presence” as “new presence”—if you want the model to bring new topics into the conversation, increase the presence penalty to discourage old ones from sticking around.
AI-900 Practice Question: Describe features of generative AI workloads on Azure
This AI-900 practice question tests your understanding of describe features of generative ai workloads on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A developer uses Azure OpenAI Service to generate conversation scripts for a chatbot. The developer wants to encourage the model to introduce new topics and avoid repeatedly discussing the same subject matter. Which parameter should the developer increase?
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
Presence penalty
The Presence penalty parameter penalizes tokens that have already appeared in the conversation, encouraging the model to introduce new topics and avoid repetition. By increasing this value, the developer reduces the likelihood of the model reusing the same subject matter, which is exactly the requirement described.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Temperature
Why it's wrong here
Temperature controls the randomness of token selection. Higher values increase creativity but do not specifically encourage the model to introduce new topics or avoid repeating themes.
- ✗
Top_p (nucleus sampling)
Why it's wrong here
Top_p limits token selection to the smallest set of tokens whose cumulative probability exceeds a threshold. It influences diversity but does not directly penalize the repetition of topics.
- ✗
Frequency penalty
Why it's wrong here
Frequency penalty penalizes tokens that have already been used, reducing word-level repetition. It does not specifically discourage the model from returning to the same broader topic.
- ✓
Presence penalty
Why this is correct
Presence penalty penalizes tokens that have already appeared in the generated output, which discourages the model from repeating the same ideas or discussing the same topics repeatedly, thereby encouraging new content.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse Presence penalty (which penalizes any repetition of a topic) with Frequency penalty (which penalizes repeated word-level occurrences), leading them to select the wrong parameter for topic novelty.
Detailed technical explanation
How to think about this question
Under the hood, Presence penalty applies a fixed additive penalty (scaled by the parameter value) to any token that has already appeared in the sequence, regardless of how many times it has appeared. This is distinct from Frequency penalty, which scales the penalty proportionally to the token's count. In real-world chatbot scenarios, increasing Presence penalty (e.g., from 0 to 0.5) helps maintain conversational freshness, while Temperature and Top_p are better suited for controlling overall response creativity.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Questions learners often ask
What does this AI-900 question test?
Describe features of generative AI workloads on Azure — This question tests Describe features of generative AI workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Presence penalty — The Presence penalty parameter penalizes tokens that have already appeared in the conversation, encouraging the model to introduce new topics and avoid repetition. By increasing this value, the developer reduces the likelihood of the model reusing the same subject matter, which is exactly the requirement described.
What should I do if I get this AI-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
This AI-900 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-900 exam.
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