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 multiple variations of a product description from a single prompt. They want the generated descriptions to be more creative and diverse, rather than repetitive. Which parameter should they increase to achieve this?
Increasing temperature makes the model more likely to choose less likely tokens, leading to more creative and diverse outputs.
B
Max tokens
Why wrong: Max tokens limits the total number of tokens in the output, affecting length, not creativity or diversity.
C
Top probability
Why wrong: Top probability (nucleus sampling) sets a cumulative probability threshold to filter token choices; lowering it can reduce diversity, raising it has limited effect on creativity.
D
Frequency penalty
Why wrong: Frequency penalty reduces the tendency to repeat the same words or phrases, which can increase diversity, but temperature is more directly associated with overall creativity and randomness.
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 more creative and diverse by raising the randomness of token selection. At higher temperatures (e.g., 0.8–1.0), the model assigns more weight to less probable tokens, producing varied and unexpected outputs. This directly addresses the need for diverse product descriptions rather than repetitive ones.
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 makes the model more likely to choose less likely tokens, leading to more creative and diverse outputs.
Related concept
Read the scenario before looking for a memorised answer.
✗
Max tokens
Why it's wrong here
Max tokens limits the total number of tokens in the output, affecting length, not creativity or diversity.
✗
Top probability
Why it's wrong here
Top probability (nucleus sampling) sets a cumulative probability threshold to filter token choices; lowering it can reduce diversity, raising it has limited effect on creativity.
✗
Frequency penalty
Why it's wrong here
Frequency penalty reduces the tendency to repeat the same words or phrases, which can increase diversity, but temperature is more directly associated with overall creativity and randomness.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Frequency penalty with Temperature, thinking that penalizing repetition is the primary way to increase diversity, but Temperature directly controls randomness and is the correct parameter for creative variation.
Trap categories for this question
Keyword trap
Frequency penalty reduces the tendency to repeat the same words or phrases, which can increase diversity, but temperature is more directly associated with overall creativity and randomness.
Command / output trap
Max tokens limits the total number of tokens in the output, affecting length, 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 higher temperature flattens the probability distribution, making low-probability tokens more likely to be chosen. In contrast, top-p sampling (nucleus sampling) dynamically selects a set of tokens whose cumulative probability exceeds a threshold, which can still yield repetitive outputs if the threshold is high. Real-world scenarios like A/B testing marketing copy benefit from high Temperature to explore a wider creative space, while low Temperature (e.g., 0.1) is used for factual or deterministic tasks.
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 more creative and diverse by raising the randomness of token selection. At higher temperatures (e.g., 0.8–1.0), the model assigns more weight to less probable tokens, producing varied and unexpected outputs. This directly addresses the need for diverse product descriptions rather than repetitive ones.
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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