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. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 writer uses Azure OpenAI Service to generate story ideas. The current configuration uses a temperature setting of 0, causing the model to produce identical outputs for the same prompt. The writer wants more creative and diverse outputs. Which parameter should be increased?
Why wrong: max_tokens limits the number of tokens in the output, affecting length but not creativity or diversity.
B
temperature
Increasing temperature increases randomness, leading to more creative and diverse outputs.
C
top_p
Why wrong: top_p (nucleus sampling) also affects diversity, but temperature is the standard parameter to control creativity; increasing temperature is more direct.
D
frequency_penalty
Why wrong: frequency_penalty reduces the likelihood of repeating the same words or phrases, which promotes diversity but does not directly increase overall creativity as temperature does.
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. A temperature of 0 makes the model deterministic, always choosing the most likely next token, which leads to identical outputs for the same prompt. Increasing the temperature (e.g., to 0.7 or higher) introduces more randomness, allowing the model to sample from less likely tokens and produce more creative, diverse story ideas.
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.
✗
max_tokens
Why it's wrong here
max_tokens limits the number of tokens in the output, affecting length but not creativity or diversity.
✓
temperature
Why this is correct
Increasing temperature increases randomness, leading to more creative and diverse outputs.
Related concept
Read the scenario before looking for a memorised answer.
✗
top_p
Why it's wrong here
top_p (nucleus sampling) also affects diversity, but temperature is the standard parameter to control creativity; increasing temperature is more direct.
✗
frequency_penalty
Why it's wrong here
frequency_penalty reduces the likelihood of repeating the same words or phrases, which promotes diversity but does not directly increase overall creativity as temperature does.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse temperature with top_p, thinking both are equally responsible for randomness, but temperature is the direct control for randomness while top_p is an alternative sampling method that can also affect diversity but is not the parameter to increase for more creative outputs.
Trap categories for this question
Keyword trap
frequency_penalty reduces the likelihood of repeating the same words or phrases, which promotes diversity but does not directly increase overall creativity as temperature does.
Command / output trap
max_tokens limits the number of tokens in the output, affecting length but not creativity or diversity.
Detailed technical explanation
How to think about this question
Temperature works by scaling the logits (raw scores) before applying the softmax function. A temperature of 0 effectively makes the softmax output a one-hot vector (always picking the highest probability token), while higher temperatures flatten the probability distribution, giving lower-probability tokens a better chance of being selected. In practice, for creative writing tasks, a temperature between 0.7 and 1.0 is commonly used, while for factual or code generation tasks, a lower temperature (e.g., 0.1–0.3) is preferred to maintain consistency.
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. A temperature of 0 makes the model deterministic, always choosing the most likely next token, which leads to identical outputs for the same prompt. Increasing the temperature (e.g., to 0.7 or higher) introduces more randomness, allowing the model to sample from less likely tokens and produce more creative, diverse story ideas.
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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