- A
frequency_penalty
Why wrong: Increasing frequency_penalty reduces the likelihood of repeating the same tokens, which decreases repetition but does not necessarily make the output more creative or diverse in word choices.
- B
presence_penalty
Why wrong: Increasing presence_penalty encourages the model to talk about new topics by penalizing tokens that have already appeared, but it does not directly increase creativity or unexpected word choices.
- C
temperature
Increasing temperature raises the randomness of token selection, leading to more creative, diverse, and surprising output. It is the primary parameter for controlling creativity.
- D
top_p
Why wrong: top_p (nucleus sampling) is used to control the cumulative probability of tokens considered. While it can affect diversity, increasing temperature is more directly associated with creativity and unexpected choices.
Quick Answer
The answer is the temperature parameter. Increasing temperature raises the probability of sampling lower-probability tokens, which forces the model to make more unexpected word choices and produce creative, diverse outputs rather than safe, predictable text. On the Microsoft Azure AI-900 exam, this concept tests your understanding of how generative AI models balance randomness and determinism—a common trap is confusing temperature with top-p (nucleus sampling), which also controls diversity but through cumulative probability rather than scaling the logits. For the marketing team’s goal of generating varied social media posts, a higher temperature value like 0.9 is the correct adjustment. Remember: higher heat means more creative heat—turn up the temperature to turn up the surprise.
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 marketing team uses Azure OpenAI Service to generate social media posts. They want the generated text to be more creative and diverse, with unexpected word choices. Which parameter should they 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
temperature
Increasing the temperature parameter makes the model more creative and diverse by raising the probability of sampling lower-probability tokens, leading to unexpected word choices. Temperature controls the randomness of token selection, with higher values (e.g., 0.9) producing more varied outputs, which aligns with the team's goal of generating creative social media posts.
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.
- ✗
frequency_penalty
Why it's wrong here
Increasing frequency_penalty reduces the likelihood of repeating the same tokens, which decreases repetition but does not necessarily make the output more creative or diverse in word choices.
- ✗
presence_penalty
Why it's wrong here
Increasing presence_penalty encourages the model to talk about new topics by penalizing tokens that have already appeared, but it does not directly increase creativity or unexpected word choices.
- ✓
temperature
Why this is correct
Increasing temperature raises the randomness of token selection, leading to more creative, diverse, and surprising output. It is the primary parameter for controlling creativity.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
top_p
Why it's wrong here
top_p (nucleus sampling) is used to control the cumulative probability of tokens considered. While it can affect diversity, increasing temperature is more directly associated with creativity and unexpected choices.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse temperature with top_p, thinking both control creativity similarly, but temperature directly affects randomness while top_p restricts the set of tokens considered, and increasing top_p can actually reduce diversity.
Trap categories for this question
Command / output trap
Increasing frequency_penalty reduces the likelihood of repeating the same tokens, which decreases repetition but does not necessarily make the output more creative or diverse in word choices.
Detailed technical explanation
How to think about this question
Temperature works by scaling the logits (raw scores) before applying the softmax function; a higher temperature (e.g., 1.5) flattens the probability distribution, making low-probability tokens more likely to be chosen. In contrast, a temperature of 0 makes the model deterministic, always picking the highest-probability token. In real-world scenarios, creative writing tasks often use temperature values between 0.7 and 1.0, while tasks requiring factual accuracy (e.g., code generation) use lower values to avoid hallucinations.
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: temperature — Increasing the temperature parameter makes the model more creative and diverse by raising the probability of sampling lower-probability tokens, leading to unexpected word choices. Temperature controls the randomness of token selection, with higher values (e.g., 0.9) producing more varied outputs, which aligns with the team's goal of generating creative social media posts.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
1 more ways this is tested on AI-900
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A marketing team uses Azure OpenAI Service to generate tagline options for a new product. They notice that the model often generates very similar taglines for the same prompt, lacking creativity. To increase the diversity and variety of the output, which parameter should they increase?
medium- ✓ A.Temperature
- B.Top P
- C.Frequency penalty
- D.Max tokens
Why A: Increasing the temperature parameter makes the model's output more random and diverse by scaling the probability distribution over possible next tokens. A higher temperature (e.g., 0.9) flattens the distribution, giving lower-probability tokens a better chance to be selected, which directly addresses the lack of creativity and variety in the generated taglines.
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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