- A
Temperature
Correct because decreasing temperature makes the model less random and more deterministic, favoring the most likely tokens and producing more predictable output.
- B
Top P
Why wrong: Incorrect because decreasing top_p can also reduce randomness by limiting the sampling pool to the most likely tokens with a cumulative probability threshold, but it is not the primary control; temperature directly affects the probability distribution's sharpness.
- C
Frequency penalty
Why wrong: Incorrect because frequency penalty reduces the likelihood of repeating the same tokens in the generated text; it does not control the overall randomness or predictability of the output.
- D
Presence penalty
Why wrong: Incorrect because presence penalty encourages the model to talk about new topics by penalizing tokens that have already appeared; it does not directly control how predictable or surprising the output is.
Quick Answer
The answer is temperature, and decreasing it is the correct parameter to make Azure OpenAI output more predictable. Temperature controls the randomness of token selection; a lower value, such as 0.2, forces the model to choose the most probable next words, producing conservative, common phrases and reducing surprising or unusual combinations. On the AI-900 exam, this concept tests your understanding of how to tune generative AI for specific business needs—here, a marketing team wanting reliable taglines. A common trap is confusing temperature with top-p (nucleus sampling), which also limits randomness but works differently by cutting off low-probability tokens entirely. Remember: lower temperature = less creativity, more predictability; think of it as turning down the “surprise dial” to get safe, expected 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 marketing team uses Azure OpenAI Service to generate taglines for a new advertising campaign. They want the output to be more predictable and less surprising, sticking to the most common phrases and avoiding unusual combinations. Which parameter should they decrease?
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
Temperature controls the randomness of the model's output. Lowering the temperature (e.g., from 1.0 to 0.2) makes the model more deterministic, favoring high-probability tokens and common phrases, which reduces surprise and unusual combinations. This directly aligns with the team's goal of predictable, conservative taglines.
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
Correct because decreasing temperature makes the model less random and more deterministic, favoring the most likely tokens and producing more predictable output.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Top P
Why it's wrong here
Incorrect because decreasing top_p can also reduce randomness by limiting the sampling pool to the most likely tokens with a cumulative probability threshold, but it is not the primary control; temperature directly affects the probability distribution's sharpness.
- ✗
Frequency penalty
Why it's wrong here
Incorrect because frequency penalty reduces the likelihood of repeating the same tokens in the generated text; it does not control the overall randomness or predictability of the output.
- ✗
Presence penalty
Why it's wrong here
Incorrect because presence penalty encourages the model to talk about new topics by penalizing tokens that have already appeared; it does not directly control how predictable or surprising the output is.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Top P with temperature, thinking both control randomness equally, but Top P controls the size of the candidate set while temperature directly scales token probabilities, making temperature the correct choice for reducing surprise and sticking to common phrases.
Trap categories for this question
Command / output trap
Incorrect because frequency penalty reduces the likelihood of repeating the same tokens in the generated text; it does not control the overall randomness or predictability of the output.
Detailed technical explanation
How to think about this question
Under the hood, temperature scales the logits (raw scores) before the softmax function: dividing by a lower temperature amplifies differences between high and low probability tokens, making the model more confident and repetitive. In contrast, Top P dynamically adjusts the vocabulary set per generation step, which can interact with temperature in complex ways; for strictly predictable outputs, lowering temperature alone is the standard approach. A real-world scenario is generating legal disclaimers where every word must be precise and avoid creative variation.
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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
Describe features of generative AI workloads on Azure — study guide chapter
Learn the concepts, then practise the questions
- →
Describe features of generative AI workloads on Azure practice questions
Targeted practice on this topic area only
- →
All AI-900 questions
1,020 questions across all exam domains
- →
Microsoft Azure AI Fundamentals AI-900 study guide
Full concept coverage aligned to exam objectives
- →
AI-900 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related AI-900 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Describe Artificial Intelligence workloads and considerations practice questions
Practise AI-900 questions linked to Describe Artificial Intelligence workloads and considerations.
Describe fundamental principles of machine learning on Azure practice questions
Practise AI-900 questions linked to Describe fundamental principles of machine learning on Azure.
Describe features of computer vision workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of computer vision workloads on Azure.
Describe features of Natural Language Processing workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of Natural Language Processing workloads on Azure.
Describe features of generative AI workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of generative AI workloads on Azure.
AI-900 fundamentals practice questions
Practise AI-900 questions linked to AI-900 fundamentals.
AI-900 scenario practice questions
Practise AI-900 questions linked to AI-900 scenario.
AI-900 troubleshooting practice questions
Practise AI-900 questions linked to AI-900 troubleshooting.
Practice this exam
Start a free AI-900 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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 — Temperature controls the randomness of the model's output. Lowering the temperature (e.g., from 1.0 to 0.2) makes the model more deterministic, favoring high-probability tokens and common phrases, which reduces surprise and unusual combinations. This directly aligns with the team's goal of predictable, conservative taglines.
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 →
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.