The answer is to set the temperature parameter to 0. This forces the model to select the most probable token at each step of generation, eliminating the randomness introduced by higher temperature values and ensuring deterministic output for identical prompts. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of how parameters control model behavior, often appearing in scenarios requiring reproducibility for testing or compliance. A common trap is confusing temperature with top_p or max_tokens—remember that temperature directly governs randomness, while top_p controls nucleus sampling. For the exam, think of temperature as a dial: at 0, the model becomes a strict rule-follower; at 1, it becomes a creative storyteller. A handy memory tip is “Zero equals no surprise”—when you need the exact same answer every time, temperature must be zero.
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 quality assurance team at a software company uses Azure OpenAI Service to generate compliance reports. They need the model to produce the exact same output for a given prompt every time the API is called, to ensure reproducibility during testing. Which parameter should they set to achieve this deterministic behavior?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Set temperature to 0
Setting temperature to 0 forces the model to choose the most likely token at each step, eliminating randomness and producing deterministic outputs for the same prompt. This is essential for reproducibility in testing scenarios where identical results are required across API calls.
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.
✓
Set temperature to 0
Why this is correct
Temperature controls randomness; setting it to 0 makes the model choose the most likely token every time, producing deterministic outputs.
Related concept
Read the scenario before looking for a memorised answer.
✗
Set frequency penalty to 1
Why it's wrong here
Frequency penalty reduces repetition of tokens but does not eliminate randomness; outputs may still vary.
✗
Set top_p to 1
Why it's wrong here
Top_p controls nucleus sampling; setting it to 1 includes all tokens, which still allows random choices and does not guarantee determinism.
✗
Set max_tokens to the expected output length
Why it's wrong here
Max_tokens limits the length of the output but does not affect the randomness of token selection.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse parameters that reduce variability (like frequency penalty or top_p=1) with the one that eliminates it entirely (temperature=0), assuming any penalty or high probability threshold ensures determinism.
Trap categories for this question
Command / output trap
Frequency penalty reduces repetition of tokens but does not eliminate randomness; outputs may still vary.
Detailed technical explanation
How to think about this question
Temperature controls the softmax distribution over logits: at 0, the model always selects the token with the highest probability (argmax), making generation deterministic. In contrast, top_p (nucleus sampling) dynamically selects a subset of tokens whose cumulative probability exceeds the threshold, and even with top_p=1, the model still samples from the full distribution unless temperature is also 0. Real-world testing pipelines often set temperature=0 and seed the random generator (if supported) to guarantee bitwise identical outputs across runs.
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: Set temperature to 0 — Setting temperature to 0 forces the model to choose the most likely token at each step, eliminating randomness and producing deterministic outputs for the same prompt. This is essential for reproducibility in testing scenarios where identical results are required across API calls.
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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Question Discussion
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