AI-900 Practice Question: Describe features of generative AI workloads on Azure
A developer uses Azure OpenAI Service to generate marketing copy. They want the model to produce more focused and deterministic responses, reducing the variety of outputs for the same prompt. Which parameter should the developer decrease?
⚠ Common exam trap
A common mix-up: candidates confuse temperature with Top P, thinking both control randomness identically, but temperature directly scales logits while Top P sets a cumulative probability cutoff for token selection.
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. Lowering temperature (e.g., from 1.0 to 0.2) makes the model more deterministic and focused, reducing output variety for the same prompt. This is the correct parameter to adjust for more consistent marketing copy.
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 controls the softmax sampling distribution directly. Lowering it shrinks the probability gap between the most likely and less likely tokens, so the model consistently picks higher-probability continuations. For marketing copy, a low temperature keeps the output on-message, on-brand, and more deterministic; at values near zero, sampling becomes almost greedy, producing the same output for the same prompt. This is exactly the parameter to adjust when you need focused, predictable copy.
- ✗
Max tokens
Why it's wrong here
Max tokens only bounds the number of tokens generated in a single completion, not how those tokens are chosen. If the limit is hit, the response can be truncated mid-sentence, but the probability distribution over possible next tokens is completely unaffected. Therefore it can prevent long-winded marketing copy, but it cannot make the model more deterministic or keep the copy focused on a chosen angle.
When this WOULD be correct
A developer wants to ensure the model's response does not exceed a specific length, such as generating a tweet under 280 characters. Decreasing max tokens would be the correct parameter to cap the output length.
- ✗
Top P
Why it's wrong here
Top p, or nucleus sampling, restricts the candidate pool to the smallest set of tokens whose cumulative probability is at least p. Decreasing top_p eliminates low-probability outliers, so the model stops considering very unlikely words and the text becomes less diverse. It does influence randomness, but temperature is the more direct control for overall determinism because it rescales every token's logits before sampling, whereas top_p only determines which tail of the distribution is eligible.
When this WOULD be correct
A question asks: 'A developer wants to ensure the model only considers the most likely tokens during generation, ignoring low-probability tokens entirely. Which parameter should they adjust?' In that case, decreasing Top P (e.g., from 1 to 0.1) would be correct, as it limits the cumulative probability mass of token candidates.
- ✗
Frequency penalty
Why it's wrong here
Frequency penalty applies an additive penalty to tokens proportional to how many times they have already appeared in the sequence, biasing the model against repetitive vocabulary. It alters the raw scores of tokens dynamically but leaves the underlying stochasticity of sampling untouched; the model can still be quite random while simply avoiding repeated phrases. That makes it useful for preventing churn in marketing copy, not for making the generation deterministic or tightly focused on a single message.
When this WOULD be correct
A developer wants to reduce repetitive phrases in generated text. Decreasing frequency penalty would be correct because it lowers the penalty for using tokens that have already appeared, thus reducing the model's tendency to avoid repetition.
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 controls the softmax sampling distribution directly. Lowering it shrinks the probability gap between the most likely and less likely tokens, so the model consistently picks higher-probability continuations. For marketing copy, a low temperature keeps the output on-message, on-brand, and more deterministic; at values near zero, sampling becomes almost greedy, producing the same output for the same prompt. This is exactly the parameter to adjust when you need focused, predictable copy.
✗Max tokensWrong answer — click to see why▾
Why this is wrong here
Decreasing max tokens limits the length of the response but does not affect the randomness or variety of outputs for the same prompt. It controls the maximum number of tokens generated, not the determinism.
★ When this WOULD be the correct answer
A developer wants to ensure the model's response does not exceed a specific length, such as generating a tweet under 280 characters. Decreasing max tokens would be the correct parameter to cap the output length.
Why candidates choose this
Candidates may mistakenly think that reducing the maximum token count will make the model more focused by cutting off less likely continuations, but it only truncates the response without affecting the sampling randomness.
✗Top PWrong answer — click to see why▾
Why this is wrong here
Decreasing Top P reduces the set of tokens considered for sampling, which also makes outputs more deterministic, but the question specifically asks for reducing variety in outputs, which is directly controlled by temperature. Temperature is the primary parameter for controlling randomness; Top P is an alternative sampling strategy that can be used alongside temperature.
★ When this WOULD be the correct answer
A question asks: 'A developer wants to ensure the model only considers the most likely tokens during generation, ignoring low-probability tokens entirely. Which parameter should they adjust?' In that case, decreasing Top P (e.g., from 1 to 0.1) would be correct, as it limits the cumulative probability mass of token candidates.
Why candidates choose this
Candidates may confuse Top P with temperature because both control randomness, but Top P is less commonly understood. They might think decreasing Top P is the primary way to reduce output variety, not realizing temperature is the more direct parameter.
✗Frequency penaltyWrong answer — click to see why▾
Why this is wrong here
Decreasing frequency penalty reduces the penalty for repeated tokens, which can increase repetition but does not directly reduce output variety or make responses more deterministic. It affects repetition, not the randomness of token selection.
★ When this WOULD be the correct answer
A developer wants to reduce repetitive phrases in generated text. Decreasing frequency penalty would be correct because it lowers the penalty for using tokens that have already appeared, thus reducing the model's tendency to avoid repetition.
Why candidates choose this
Candidates may confuse frequency penalty with temperature, thinking that penalizing frequent tokens will make outputs more focused, but frequency penalty actually controls repetition, not 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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