AI-900 Practice Question: Describe features of generative AI workloads on Azure
A company uses Azure OpenAI Service to generate creative product descriptions. They want to increase the randomness and variety of the generated outputs to produce more diverse suggestions. Which parameter should they increase?
⚠ Common exam trap
It's easy for candidates to confuse temperature with Top_p, assuming both control randomness equally, but temperature directly scales logits while Top_p filters the token set by cumulative probability—a subtle but critical distinction tested in AI-900.
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
Temperature controls the randomness of the model's output by scaling the logits before applying the softmax function. Increasing temperature (e.g., from 0.7 to 1.0) flattens the probability distribution, making lower-probability tokens more likely to be chosen, which increases diversity and creativity in generated text.
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 scales the logits by division before the softmax transform, flattening the probability distribution and raising its entropy. This directly makes the model sample from a wider range of lower-probability tokens, which is precisely why it is the primary parameter for controlling overall randomness and creative variety.
- ✗
Top_p
Why it's wrong here
Top_p (nucleus sampling) selects from the smallest set of tokens whose cumulative probability exceeds the threshold p, cutting off the long tail of unlikely tokens. While varying p can change how many candidates are considered, it is a sampling strategy that locally reshapes the candidate set rather than globally raising entropy like temperature. Temperature is the more direct and commonly recommended control for overall randomness.
When this WOULD be correct
A question asks: 'You want to ensure the model only considers tokens with a cumulative probability of 0.9, ignoring very unlikely tokens. Which parameter should you adjust?' In that case, increasing top_p (or setting it to 0.9) would be correct.
- ✗
Frequency penalty
Why it's wrong here
Frequency penalty modifies the logits by subtracting a value proportional to how many times each token has already appeared in the output. This specifically suppresses repeated tokens, reducing repetition, but it does not alter the baseline entropy of the distribution—the randomness of the generation remains governed by other settings. It can force the model to consider alternatives, but that is a secondary effect, not a direct randomization mechanism.
When this WOULD be correct
When the question asks: 'Which parameter should be increased to reduce the repetition of words or phrases in generated text?' In that case, increasing frequency penalty would be correct.
- ✗
Presence penalty
Why it's wrong here
Presence penalty subtracts a fixed value from every token that has appeared at least once, independent of its frequency. This encourages the model to introduce entirely new tokens to avoid the penalty, but it does not increase the entropy of the underlying probability distribution; it only nudges the model away from previously used tokens. Thus it is a repetition-reduction tool, not a randomness-enhancing parameter.
When this WOULD be correct
A question asks: 'You want to reduce the repetition of specific topics or entities in generated text to encourage the model to talk about new subjects. Which parameter should you increase?' In that scenario, presence penalty is the correct answer.
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
Increasing temperature scales the logits by division before the softmax transform, flattening the probability distribution and raising its entropy. This directly makes the model sample from a wider range of lower-probability tokens, which is precisely why it is the primary parameter for controlling overall randomness and creative variety.
✗Top_pWrong answer — click to see why▾
Why this is wrong here
Top_p controls nucleus sampling, which limits the cumulative probability of token choices, not randomness. Increasing temperature directly increases randomness, while increasing top_p reduces diversity by restricting the set of likely tokens.
★ When this WOULD be the correct answer
A question asks: 'You want to ensure the model only considers tokens with a cumulative probability of 0.9, ignoring very unlikely tokens. Which parameter should you adjust?' In that case, increasing top_p (or setting it to 0.9) would be correct.
Why candidates choose this
Candidates may confuse top_p with temperature because both affect output diversity, but top_p controls the size of the candidate set rather than the randomness of selection within that set.
✗Frequency penaltyWrong answer — click to see why▾
Why this is wrong here
Frequency penalty reduces repetition by penalizing tokens that have already appeared in the text, which does not directly increase randomness or variety; it only discourages repeating the same words or phrases.
★ When this WOULD be the correct answer
When the question asks: 'Which parameter should be increased to reduce the repetition of words or phrases in generated text?' In that case, increasing frequency penalty would be correct.
Why candidates choose this
Candidates may confuse 'reducing repetition' with 'increasing variety', or think that penalizing frequent tokens will force the model to generate more diverse outputs, but the primary effect is on repetition, not overall randomness.
✗Presence penaltyWrong answer — click to see why▾
Why this is wrong here
Increasing presence penalty reduces the likelihood of repeating the same topics, which can increase diversity but does not directly control randomness or variety in the same way temperature does. Temperature directly adjusts the probability distribution for token selection, making outputs more random when increased.
★ When this WOULD be the correct answer
A question asks: 'You want to reduce the repetition of specific topics or entities in generated text to encourage the model to talk about new subjects. Which parameter should you increase?' In that scenario, presence penalty is the correct answer.
Why candidates choose this
Candidates may confuse 'presence penalty' with increasing variety, as both can lead to more diverse outputs, but they operate through different mechanisms—presence penalizes topic repetition rather than increasing randomness.
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?”
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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.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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