AI-900 Practice Question: Describe features of generative AI workloads on Azure
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?
⚠ Common exam trap
Watch out — 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.
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 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.
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 directly scales the logits (raw scores) of every candidate token before the softmax layer is applied. A higher temperature flattens the probability distribution, allowing lower-ranked tokens to be selected more often, which results in more creative, varied, and even unpredictable text. Lowering temperature concentrates probability mass on the top tokens, making outputs more deterministic and coherent. This is the primary sampling setting Azure OpenAI exposes for controlling overall randomness vs. conservatism.
- ✗
Max tokens
Why it's wrong here
Max tokens is a hard cap on the number of tokens the model can generate in a single response, so it only controls the length of the output, not the underlying probability distribution. Because it does not alter the relative likelihood of choosing any token at each step, it can neither increase creativity nor add diversity; it merely truncates the generated sequence once the token limit is reached. Limiting max tokens can actually force a cut-off mid-sentence, but that is a practical constraint, not a semantic or stylistic control.
When this WOULD be correct
A question asks: 'A developer needs to generate a detailed product description of at least 500 words. Which parameter should they adjust to ensure the output is long enough?' In that scenario, increasing max tokens would be correct.
- ✗
Top probability
Why it's wrong here
Top probability (often called nucleus sampling) restricts the pool of next-token candidates to the smallest set whose cumulative probability exceeds the given p-value, such as 0.9. Lowering p makes the model choose only from the most likely tokens, which reduces randomness and yields more predictable text; raising p includes more long-tail tokens, but the effect saturates as p approaches 1. This parameter is a truncation of the candidate set, not a scaling of token scores, so it changes the sampling budget rather than directly controlling the overall creativity level the way temperature does.
When this WOULD be correct
A question asks: 'You want to ensure that the generated text uses only the most likely tokens, avoiding rare or unusual words. Which parameter should you adjust?' In that case, decreasing top probability (or setting it to a low value) would be correct.
- ✗
Frequency penalty
Why it's wrong here
Frequency penalty adjusts the logit scores of tokens proportionally to how many times they have already appeared in the generated output, discouraging the model from repeating the same words or phrases. This targeted penalty can increase linguistic variety by mitigating repetitive loops, but it does not broadly loosen the model's token-selection behavior like temperature does. It is a corrective mechanism for repetition only; it leaves the probabilities of never-seen tokens untouched and thus is not a general creativity dial.
When this WOULD be correct
A question asks: 'A chatbot generates repetitive responses. Which parameter should be increased to reduce repetition of the same words or phrases?' In that context, frequency penalty is correct because it penalizes frequently used tokens.
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 directly scales the logits (raw scores) of every candidate token before the softmax layer is applied. A higher temperature flattens the probability distribution, allowing lower-ranked tokens to be selected more often, which results in more creative, varied, and even unpredictable text. Lowering temperature concentrates probability mass on the top tokens, making outputs more deterministic and coherent. This is the primary sampling setting Azure OpenAI exposes for controlling overall randomness vs. conservatism.
✗Max tokensWrong answer — click to see why▾
Why this is wrong here
Increasing max tokens only extends the length of the generated text, not its creativity or diversity. It does not affect how varied or surprising the outputs are.
★ When this WOULD be the correct answer
A question asks: 'A developer needs to generate a detailed product description of at least 500 words. Which parameter should they adjust to ensure the output is long enough?' In that scenario, increasing max tokens would be correct.
Why candidates choose this
Candidates may think that allowing more tokens gives the model more 'room' to be creative, confusing output length with output diversity.
✗Top probabilityWrong answer — click to see why▾
Why this is wrong here
Top probability (nucleus sampling) controls the cumulative probability threshold for token selection, which can reduce randomness by limiting the pool of possible tokens. Increasing it does not directly increase creativity or diversity; it may even make outputs less diverse by excluding low-probability tokens.
★ When this WOULD be the correct answer
A question asks: 'You want to ensure that the generated text uses only the most likely tokens, avoiding rare or unusual words. Which parameter should you adjust?' In that case, decreasing top probability (or setting it to a low value) would be correct.
Why candidates choose this
Candidates may confuse 'top probability' with 'temperature' because both influence randomness, but top probability is a different mechanism that can actually reduce diversity when increased.
✗Frequency penaltyWrong answer — click to see why▾
Why this is wrong here
Increasing frequency penalty reduces repetition by penalizing tokens that have already appeared, but it does not directly increase creativity or diversity of generated content; temperature is the parameter that controls randomness and creativity.
★ When this WOULD be the correct answer
A question asks: 'A chatbot generates repetitive responses. Which parameter should be increased to reduce repetition of the same words or phrases?' In that context, frequency penalty is correct because it penalizes frequently used tokens.
Why candidates choose this
Candidates may confuse 'penalty' with 'creativity' or think that penalizing frequency will force the model to generate more diverse content, but frequency penalty specifically targets repetition of tokens, not overall creativity or diversity of ideas.
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
Azure OpenAI Service
Azure OpenAI Service is a cloud platform from Microsoft that lets developers use powerful artificial intelligence models, like GPT-4, to build applications that can understand and generate human-like text, code, images, and more.
Key term
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
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Senior Network & Security Engineer · founder of Courseiva
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