AI-900 Practice Question: Describe features of generative AI workloads on Azure
A marketing team uses Azure OpenAI Service to generate tagline options for a new product. They notice that the model often generates very similar taglines for the same prompt, lacking creativity. To increase the diversity and variety of the output, which parameter should they increase?
⚠ Common exam trap
Watch out — candidates often confuse temperature with Top P, thinking both control randomness equally, but temperature directly scales the probability distribution for randomness, while Top P controls the size of the candidate token set via cumulative probability threshold.
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
Increasing the temperature parameter makes the model's output more random and diverse by scaling the probability distribution over possible next tokens. A higher temperature (e.g., 0.9) flattens the distribution, giving lower-probability tokens a better chance to be selected, which directly addresses the lack of creativity and variety in the generated taglines.
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
Temperature scales the logits before the softmax layer in the token sampling process. Increasing it flattens the probability distribution, so lower-probability tokens become more likely to be chosen, which yields more creative and varied tagline outputs. In contrast, lower temperature sharpens the distribution toward the most likely token, making responses more deterministic.
- ✗
Top P
Why it's wrong here
Top P (nucleus sampling) limits generation to the smallest set of tokens whose cumulative probability reaches the threshold P, pruning the long tail of unlikely alternatives. While this can increase output consistency by eliminating highly improbable tokens, it either narrows or broadens the candidate pool rather than rescaling the full distribution. Temperature, on the other hand, directly adjusts the shape of the entire probability distribution, making it the more direct control for increasing randomness and creative variability.
When this WOULD be correct
A question asks: 'To ensure the model selects from a narrower, more focused set of tokens, which parameter should be decreased?' In that case, decreasing Top P would be correct.
- ✗
Frequency penalty
Why it's wrong here
Frequency penalty reduces repetition by subtracting a value proportional to the number of times a token has already appeared in the sequence, making reused words less likely to be selected again. This mechanism specifically targets repetitive patterns and improves linguistic variety in n-grams, but it does not increase overall creative randomness or the diversity of thematic ideas. As a result, it is a corrective measure for repetition, not a general-purpose creativity control like temperature.
When this WOULD be correct
In a scenario where the model is generating repetitive phrases or loops (e.g., repeating the same words or sentences), increasing frequency penalty would be correct to discourage token repetition and improve output variety.
- ✗
Max tokens
Why it's wrong here
Max tokens defines the upper bound on the number of tokens the Azure OpenAI service can generate in a single response, acting as a hard length limit. It does not modify the underlying probability distribution or the sampling mechanism, so it cannot affect how diverse or creative the taglines are. Reducing or increasing this limit simply truncates or extends the text length without altering the relative probabilities of candidate tokens.
When this WOULD be correct
A question asks: 'The marketing team wants the model to generate longer taglines, up to 100 words. Which parameter should they adjust?' In that case, increasing max tokens would allow the model to produce longer responses.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The AI-900 exam frequently reuses these exact scenarios with slightly different constraints.
✓TemperatureCorrect answer▾
Why this is correct
Temperature scales the logits before the softmax layer in the token sampling process. Increasing it flattens the probability distribution, so lower-probability tokens become more likely to be chosen, which yields more creative and varied tagline outputs. In contrast, lower temperature sharpens the distribution toward the most likely token, making responses more deterministic.
✗Top PWrong answer — click to see why▾
Why this is wrong here
Top P controls the cumulative probability of token selection, not the diversity of the output. Increasing Top P can still result in similar outputs if the model's probability distribution is concentrated on a few tokens.
★ When this WOULD be the correct answer
A question asks: 'To ensure the model selects from a narrower, more focused set of tokens, which parameter should be decreased?' In that case, decreasing Top P would be correct.
Why candidates choose this
Candidates may confuse Top P with temperature, as both affect randomness, but Top P is about nucleus sampling rather than scaling logits, leading to a misunderstanding of their distinct roles.
✗Frequency penaltyWrong answer — click to see why▾
Why this is wrong here
Increasing frequency penalty reduces repetition of tokens, which can increase diversity, but it primarily penalizes tokens that have already appeared, not directly controlling the randomness of the output. For generating more creative and varied taglines, adjusting temperature is more effective.
★ When this WOULD be the correct answer
In a scenario where the model is generating repetitive phrases or loops (e.g., repeating the same words or sentences), increasing frequency penalty would be correct to discourage token repetition and improve output variety.
Why candidates choose this
Candidates may confuse frequency penalty with temperature, thinking both increase diversity, but frequency penalty specifically targets repetition rather than overall randomness.
✗Max tokensWrong answer — click to see why▾
Why this is wrong here
Increasing max tokens only extends the length of the generated text, not the diversity or creativity of the output. It does not affect how the model samples from the probability distribution.
★ When this WOULD be the correct answer
A question asks: 'The marketing team wants the model to generate longer taglines, up to 100 words. Which parameter should they adjust?' In that case, increasing max tokens would allow the model to produce longer responses.
Why candidates choose this
Candidates may think that more tokens allow the model to 'explore' more ideas, but max tokens only sets a length limit, not the sampling behavior.
Analysis generated from the official AI-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
Go deeper
Related to this question
Learn chapter
Azure Machine Learning Studio
Key term
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
Key term
Service
A service is a software component or system that performs a specific function and is available to be used by other programs or users over a network.
About these practice questions
This AI-900 question is part of Courseiva's 985-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.