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 code snippets. They need the model to produce the most likely completion each time, with no randomness or creativity. Which parameter should they set?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
A temperature of 0 makes the model deterministic, always picking the most probable next token, resulting in the most likely output with no randomness.
B
temperature = 1
Why wrong: A temperature of 1 introduces maximal randomness, producing varied outputs, which is the opposite of what the developer wants.
C
top_p = 0.5
Why wrong: Top_p (nucleus sampling) limits token selection to a cumulative probability mass of 0.5, but still introduces randomness; it does not guarantee deterministic output.
D
frequency_penalty = 0.5
Why wrong: Frequency penalty reduces token repetition but does not control randomness; the model can still produce varied outputs.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
temperature = 0
Setting temperature = 0 forces the model to always select the token with the highest probability at each step, eliminating randomness and ensuring deterministic, most-likely completions. This is ideal for tasks like code generation where consistency and predictability are required, as it disables the sampling randomness that higher temperature values introduce.
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 = 0
Why this is correct
A temperature of 0 makes the model deterministic, always picking the most probable next token, resulting in the most likely output with no randomness.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
temperature = 1
Why it's wrong here
A temperature of 1 introduces maximal randomness, producing varied outputs, which is the opposite of what the developer wants.
✗
top_p = 0.5
Why it's wrong here
Top_p (nucleus sampling) limits token selection to a cumulative probability mass of 0.5, but still introduces randomness; it does not guarantee deterministic output.
✗
frequency_penalty = 0.5
Why it's wrong here
Frequency penalty reduces token repetition but does not control randomness; the model can still produce varied outputs.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Microsoft often tests the misconception that temperature = 1 is 'neutral' or 'default' and therefore deterministic, but in reality temperature = 1 is the default for creative tasks and introduces full randomness, while temperature = 0 is the only setting that guarantees the most likely completion every time.
Trap categories for this question
Command / output trap
A temperature of 1 introduces maximal randomness, producing varied outputs, which is the opposite of what the developer wants.
Detailed technical explanation
How to think about this question
Temperature controls the softmax scaling of logits before sampling: at temperature = 0, the softmax becomes a step function that assigns probability 1 to the highest logit and 0 to all others, making the output deterministic. In contrast, temperature > 0 flattens or sharpens the distribution, enabling sampling from lower-probability tokens. For code generation, even slight randomness (e.g., temperature = 0.1) can produce syntactically valid but functionally different code, so temperature = 0 is critical for reproducibility in production pipelines.
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 = 0 — Setting temperature = 0 forces the model to always select the token with the highest probability at each step, eliminating randomness and ensuring deterministic, most-likely completions. This is ideal for tasks like code generation where consistency and predictability are required, as it disables the sampling randomness that higher temperature values introduce.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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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