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 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?
Increasing temperature raises randomness, making the model generate more creative and less predictable outputs.
B
Top p
Why wrong: Top p controls cumulative probability threshold, also affecting diversity, but temperature is more directly associated with creativity.
C
Frequency penalty
Why wrong: Frequency penalty reduces the likelihood of repeating the same words, which can increase novelty but not overall creativity as much as temperature.
D
Presence penalty
Why wrong: Presence penalty penalizes any word that has already appeared, encouraging new topics but not necessarily increasing creativity.
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'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.
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 randomness, making the model generate more creative and less predictable outputs.
Related concept
Read the scenario before looking for a memorised answer.
✗
Top p
Why it's wrong here
Top p controls cumulative probability threshold, also affecting diversity, but temperature is more directly associated with creativity.
✗
Frequency penalty
Why it's wrong here
Frequency penalty reduces the likelihood of repeating the same words, which can increase novelty but not overall creativity as much as temperature.
✗
Presence penalty
Why it's wrong here
Presence penalty penalizes any word that has already appeared, encouraging new topics but not necessarily increasing creativity.
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 randomness equally, but Temperature directly adjusts the softmax distribution's sharpness while Top p only limits the sampling pool.
Detailed technical explanation
How to think about this question
Temperature works by scaling the logits (raw scores) before applying the softmax function; higher temperatures flatten the probability distribution, making low-probability tokens more likely to be chosen. In contrast, Top p dynamically selects the smallest set of tokens whose cumulative probability exceeds the threshold, which can produce diverse outputs but may still be deterministic if the distribution is sharp. A real-world scenario: for brainstorming fantasy plots, a Temperature of 1.2 combined with a moderate Top p (0.9) often yields the most novel ideas without descending into gibberish.
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 — 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.
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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Question Discussion
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