The answer is temperature. Decreasing the temperature parameter in Azure OpenAI directly reduces the randomness of the model’s token selection, forcing it to choose the most probable next word rather than exploring less likely alternatives. This shift toward lower entropy makes the output more deterministic and focused, which is exactly what a developer needs when generating data transformation scripts where logical consistency is critical. On the AI-900 exam, this concept tests your understanding of how to control model behavior for different tasks—creative writing uses higher temperature, while precise, repeatable outputs require lower values. A common trap is confusing temperature with top-p or max tokens, but remember: temperature governs the “creativity dial.” For a quick memory tip, think “Low temp, low risk—high temp, high surprise.”
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 developer uses Azure OpenAI Service to generate data transformation scripts. The generated scripts sometimes contain logical errors. To make the model's output more deterministic and reduce variability, which parameter should the developer decrease?
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 0.7 to 0.1) makes the model more deterministic and focused, reducing variability and the likelihood of logical errors in generated scripts. This is the correct parameter to adjust for more consistent, less creative responses.
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
Correct. Decreasing Temperature reduces randomness, making the model more conservative and deterministic.
Related concept
Read the scenario before looking for a memorised answer.
✗
Top_p
Why it's wrong here
Decreasing Top_p also reduces variability by limiting the token pool, but Temperature has a more direct effect on determinism.
✗
Frequency penalty
Why it's wrong here
Frequency penalty reduces repetition by penalizing tokens that have already appeared, not overall randomness.
✗
Presence penalty
Why it's wrong here
Presence penalty encourages the model to talk about new topics, but does not directly control determinism.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Top_p with temperature, thinking both control randomness equally, but Top_p affects the diversity of token selection via cumulative probability, not the sharpness of the probability distribution, making temperature the direct control for determinism.
Detailed technical explanation
How to think about this question
Temperature works by scaling the logits (raw scores) before applying softmax; lower temperatures (e.g., 0.1) amplify the highest-probability token, making the output nearly greedy, while higher temperatures (e.g., 1.0) flatten the distribution, increasing randomness. In practice, for code generation tasks, a temperature of 0.0 to 0.2 is often used to ensure deterministic, reliable scripts, while creative tasks like story generation use higher values. The Azure OpenAI Service default temperature is 0.7, which is too high for deterministic script generation.
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 — Temperature controls the randomness of the model's output. Lowering temperature (e.g., from 0.7 to 0.1) makes the model more deterministic and focused, reducing variability and the likelihood of logical errors in generated scripts. This is the correct parameter to adjust for more consistent, less creative responses.
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.
About these practice questions
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Variation 1. A developer is using Azure OpenAI Service to generate product descriptions. They want the output to be highly focused and deterministic, with less randomness. Which parameter should they decrease?
easy
✓ A.Temperature
B.Max tokens
C.Top-p
D.Frequency penalty
Why A: Temperature controls the randomness of the model's output. Lowering the temperature (e.g., from 1.0 to 0.2) makes the model more deterministic by reducing the probability of sampling less likely tokens, resulting in more focused and predictable responses.
Last reviewed: Jun 11, 2026
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