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 developer is using Azure OpenAI to generate creative product descriptions. The outputs are often repetitive and lack variety. The developer wants to increase the diversity of the generated text while still keeping it coherent. Which parameter should the developer increase?
Increasing the temperature parameter raises randomness, leading to more diverse and less repetitive text. This is the standard way to increase creativity in outputs.
B
Top_p
Why wrong: Increasing top_p (nucleus sampling) can also increase diversity, but it is often used together with temperature. Temperature is the primary parameter for controlling randomness.
C
Max_tokens
Why wrong: Max_tokens controls the maximum length of the output, not the diversity or repetitiveness.
D
Frequency_penalty
Why wrong: Frequency_penalty reduces the likelihood of repeating the same words or phrases, which can reduce repetitiveness but does not directly increase overall diversity.
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 by amplifying the probability of less likely tokens, which increases diversity and reduces repetition. A higher temperature (e.g., 0.9) flattens the probability distribution, allowing the model to choose more varied words while still maintaining coherence, as long as the temperature is not set too high (e.g., above 1.0).
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 the temperature parameter raises randomness, leading to more diverse and less repetitive text. This is the standard way to increase creativity in outputs.
Related concept
Read the scenario before looking for a memorised answer.
✗
Top_p
Why it's wrong here
Increasing top_p (nucleus sampling) can also increase diversity, but it is often used together with temperature. Temperature is the primary parameter for controlling randomness.
✗
Max_tokens
Why it's wrong here
Max_tokens controls the maximum length of the output, not the diversity or repetitiveness.
✗
Frequency_penalty
Why it's wrong here
Frequency_penalty reduces the likelihood of repeating the same words or phrases, which can reduce repetitiveness but does not directly increase overall diversity.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse temperature with frequency_penalty, thinking that penalizing repeated words (frequency_penalty) is the primary way to increase diversity, when in fact temperature directly controls the randomness of token selection.
Trap categories for this question
Keyword trap
Frequency_penalty reduces the likelihood of repeating the same words or phrases, which can reduce repetitiveness but does not directly increase overall diversity.
Command / output trap
Max_tokens controls the maximum length of the output, not the diversity or repetitiveness.
Detailed technical explanation
How to think about this question
Temperature works by scaling the logits (raw scores) before the softmax function: a higher temperature divides the logits by a larger value, making the softmax output more uniform and thus increasing the chance of selecting lower-probability tokens. In practice, developers often combine temperature with top_p to fine-tune creativity—for example, setting temperature to 0.8 and top_p to 0.9 can yield diverse yet coherent outputs for marketing copy. A real-world scenario is generating multiple product taglines where a temperature of 0.7–0.9 produces varied but still sensible phrases, while a temperature of 0.2 would yield near-identical outputs.
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 by amplifying the probability of less likely tokens, which increases diversity and reduces repetition. A higher temperature (e.g., 0.9) flattens the probability distribution, allowing the model to choose more varied words while still maintaining coherence, as long as the temperature is not set too high (e.g., above 1.0).
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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