Question 33 of 1,020

Describe features of generative AI workloads on Azure →hardMultiple ChoiceObjective-mapped

Quick Answer

The answer is the temperature parameter. Setting temperature to the lowest possible value, which is 0, forces the model to become maximally deterministic by always selecting the token with the highest probability, eliminating any randomness or creative variation in the output. This is essential for a legal chatbot that must produce factual, non-speculative answers based strictly on internal policy documents. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of how to control model behavior for different use cases—a common trap is confusing temperature with top_p, which also controls randomness but through cumulative probability rather than direct scaling. Remember that temperature governs the “creativity dial”: turn it all the way down to zero for deterministic, fact-based responses, and turn it up for tasks like brainstorming or storytelling. A simple memory tip: “Zero temperature, zero creativity—perfect for legal certainty.”

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 is using Azure OpenAI with GPT-4 to build a chatbot that answers legal questions based on a company's internal policy documents. The developer wants the model's responses to be maximally deterministic and factual, avoiding any creative or speculative language. Which parameter should the developer set to the lowest possible value in the API call?

Question 1hardmultiple choice

Full question →

A
Temperature
Temperature controls the randomness of the model's output. Lower values (down to 0) make the model more deterministic and factual, reducing creative or speculative language.
B
Frequency penalty
Why wrong: Frequency penalty reduces the likelihood of repeating the same words or phrases. While it can improve diversity, it does not control overall randomness or creativity.
C
Presence penalty
Why wrong: Presence penalty encourages the model to talk about new topics by penalizing tokens that have already appeared. It affects topic coverage, not determinism.
D
Top_p
Why wrong: Top_p (nucleus sampling) also affects randomness by considering only the top tokens with a cumulative probability mass. However, Temperature is the more direct and commonly used parameter for achieving deterministic output.

Full breakdown with real-world context →

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. Setting it to the lowest possible value (0) makes the model deterministic, always choosing the most likely next token, which is ideal for factual, non-creative responses like legal answers. Higher temperature values introduce variability and creativity, which would be undesirable for this use case.

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
Temperature controls the randomness of the model's output. Lower values (down to 0) make the model more deterministic and factual, reducing creative or speculative language.
Related concept
Read the scenario before looking for a memorised answer.
✗
Frequency penalty
Why it's wrong here
Frequency penalty reduces the likelihood of repeating the same words or phrases. While it can improve diversity, it does not control overall randomness or creativity.
✗
Presence penalty
Why it's wrong here
Presence penalty encourages the model to talk about new topics by penalizing tokens that have already appeared. It affects topic coverage, not determinism.
✗
Top_p
Why it's wrong here
Top_p (nucleus sampling) also affects randomness by considering only the top tokens with a cumulative probability mass. However, Temperature is the more direct and commonly used parameter for achieving deterministic output.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often confuse 'randomness' with 'repetition' or 'topic diversity,' leading them to choose frequency or presence penalties, but those parameters do not enforce deterministic factual output—only temperature set to 0 does.

Trap categories for this question

Keyword trap
Frequency penalty reduces the likelihood of repeating the same words or phrases. While it can improve diversity, it does not control overall randomness or creativity.
Command / output trap
Top_p (nucleus sampling) also affects randomness by considering only the top tokens with a cumulative probability mass. However, Temperature is the more direct and commonly used parameter for achieving deterministic output.

Detailed technical explanation

How to think about this question

Under the hood, temperature scales the logits (raw scores) before the softmax function: temperature=0 effectively collapses the probability distribution to a single token (argmax), making the output deterministic. In contrast, top_p dynamically selects the smallest set of tokens whose cumulative probability exceeds the threshold, which can still yield different outputs across calls if the distribution is flat. For legal document Q&A, even slight randomness could produce incorrect or misleading answers, so temperature=0 is the standard choice.

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.

What to study next

Got this wrong? Here's your next step.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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FAQ

Questions learners often ask

What does this AI-900 question test?

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. Setting it to the lowest possible value (0) makes the model deterministic, always choosing the most likely next token, which is ideal for factual, non-creative responses like legal answers. Higher temperature values introduce variability and creativity, which would be undesirable for this use case.

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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Same concept, more angles

1 more ways this is tested on AI-900

These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.

Variation 1. A developer is using Azure OpenAI Service to generate Python code snippets. They notice that the generated code often contains repetitive function definitions and loops. Which parameter should be increased to reduce this repetition?

easy

A.Temperature
B.Max tokens
✓ C.Frequency penalty
D.Top P

Why C: The frequency penalty parameter reduces repetition by penalizing tokens that have already appeared in the generated text, making the model less likely to reuse the same functions or loops. Increasing this value directly discourages the model from generating repetitive patterns, which is exactly the issue described.

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Question Discussion

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