The answer is the temperature parameter. This is correct because temperature controls the randomness of the model’s output by scaling the logits before the softmax function; increasing the temperature flattens the probability distribution, making lower-probability tokens more likely to be chosen, which directly increases diversity and creativity in generated text. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of how to adjust model behavior for different tasks—a common trap is confusing temperature with top-p (nucleus sampling), which instead limits the cumulative probability of token choices. A helpful memory tip is to think of temperature like a creativity dial: low temperature (near 0) is rigid and predictable, while high temperature (near 1 or above) makes the model more “heated” and adventurous, producing varied and unexpected outputs.
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 company uses Azure OpenAI Service to generate creative product descriptions. They want to increase the randomness and variety of the generated outputs to produce more diverse suggestions. Which parameter should they increase?
Increasing temperature raises the entropy of the output distribution, making the model more likely to select less probable tokens, thus increasing randomness and variety.
B
Top_p
Why wrong: Top_p (nucleus sampling) can also increase diversity, but it is not the primary parameter for controlling overall randomness; temperature is more direct.
C
Frequency penalty
Why wrong: Frequency penalty reduces the likelihood of tokens that have already appeared many times, reducing repetition but not increasing overall randomness.
D
Presence penalty
Why wrong: Presence penalty reduces the likelihood of any token that has already appeared at least once, also limiting repetition rather than increasing randomness.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Temperature
Temperature controls the randomness of the model's output by scaling the logits before applying the softmax function. Increasing temperature (e.g., from 0.7 to 1.0) flattens the probability distribution, making lower-probability tokens more likely to be chosen, which increases diversity and creativity in generated text.
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 this is correct
Increasing temperature raises the entropy of the output distribution, making the model more likely to select less probable tokens, thus increasing randomness and variety.
Related concept
Read the scenario before looking for a memorised answer.
✗
Top_p
Why it's wrong here
Top_p (nucleus sampling) can also increase diversity, but it is not the primary parameter for controlling overall randomness; temperature is more direct.
✗
Frequency penalty
Why it's wrong here
Frequency penalty reduces the likelihood of tokens that have already appeared many times, reducing repetition but not increasing overall randomness.
✗
Presence penalty
Why it's wrong here
Presence penalty reduces the likelihood of any token that has already appeared at least once, also limiting repetition rather than increasing randomness.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse temperature with Top_p, assuming both control randomness equally, but temperature directly scales logits while Top_p filters the token set by cumulative probability—a subtle but critical distinction tested in AI-900.
Detailed technical explanation
How to think about this question
Under the hood, temperature modifies the logits (raw scores) by dividing them by the temperature value before softmax: a lower temperature (e.g., 0.2) sharpens the distribution, making the highest-probability token almost certain, while a higher temperature (e.g., 1.5) flattens it, giving rare tokens a fair chance. In practice, combining temperature with Top_p can fine-tune creativity: for example, setting temperature to 0.9 and Top_p to 0.95 yields diverse yet coherent outputs, whereas high temperature alone may produce gibberish if the model is not well-calibrated.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
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 — Temperature controls the randomness of the model's output by scaling the logits before applying the softmax function. Increasing temperature (e.g., from 0.7 to 1.0) flattens the probability distribution, making lower-probability tokens more likely to be chosen, which increases diversity and creativity in generated text.
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
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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 content creator uses Azure OpenAI to generate unique story ideas for a fantasy novel. They want the output to be highly creative and unpredictable, avoiding common clichés. Which parameter should they primarily increase to achieve this?
medium
✓ A.Temperature
B.Top p
C.Frequency penalty
D.Presence penalty
Why A: Increasing the Temperature parameter makes the model's output more random and less deterministic, which is ideal for generating highly creative and unpredictable story ideas. A higher temperature (e.g., 0.9–1.0) increases the probability of sampling less likely tokens, reducing repetition and clichés.
Last reviewed: Jun 11, 2026
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