The answer is temperature. Lowering the temperature parameter reduces the randomness of the model’s output, making it more deterministic and focused, which is exactly what a developer needs when generating consistent marketing copy. Technically, temperature controls the probability distribution over possible tokens; a lower value (e.g., 0.2) forces the model to choose the most likely next word, while a higher value (e.g., 1.0) allows more creative and varied choices. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of how to fine-tune generative AI responses for reliability versus creativity—a common trap is confusing temperature with top-p (nucleus sampling), which also controls randomness but via cumulative probability. Remember the memory tip: “Low temp, low risk—same prompt, same result.”
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 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?
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.
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
Lowering temperature reduces randomness, making outputs more deterministic and focused.
Related concept
Read the scenario before looking for a memorised answer.
✗
Max tokens
Why it's wrong here
Max tokens sets a limit on output length, not the determinism of the content.
✗
Top P
Why it's wrong here
Top P also controls randomness; decreasing it would reduce diversity, but temperature is the more direct parameter for focus.
✗
Frequency penalty
Why it's wrong here
Frequency penalty discourages the model from repeating the same words, it does not affect overall determinism.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Trap categories for this question
Command / output trap
Max tokens sets a limit on output length, not the determinism of the content.
Detailed technical explanation
How to think about this question
Temperature works by scaling the logits (raw scores) before applying the softmax function. A lower temperature (e.g., 0.1) amplifies the probability gap between high- and low-probability tokens, making the model almost always pick the most likely next token. In contrast, a higher temperature (e.g., 1.5) flattens the distribution, allowing less likely tokens to be chosen more often. For deterministic outputs, temperature should be set near 0, but not exactly 0 to avoid degenerate repetition in some models.
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 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.
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.
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Variation 1. A developer is using Azure OpenAI to generate creative product descriptions. The outputs are often repetitive and lack variety. The developer wants to increase the diversity of the generated text while still keeping it coherent. Which parameter should the developer increase?
hard
✓ A.Temperature
B.Top_p
C.Max_tokens
D.Frequency_penalty
Why A: Increasing the temperature parameter makes the model's output more random by amplifying the probability of less likely tokens, which increases diversity and reduces repetition. A higher temperature (e.g., 0.9) flattens the probability distribution, allowing the model to choose more varied words while still maintaining coherence, as long as the temperature is not set too high (e.g., above 1.0).
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Question Discussion
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