- A
Increase the temperature value.
Higher temperature increases randomness, leading to more diverse outputs.
- B
Decrease the top_p value.
Why wrong: Lower top_p reduces the pool of tokens considered, decreasing diversity.
- C
Increase the max_tokens value.
Why wrong: max_tokens affects length, not diversity.
- D
Increase the frequency_penalty value.
Why wrong: Frequency penalty reduces repetition but does not directly increase diversity.
Question 761 of 993
How to Use Temperature to Control Diversity and Creativity in Azure OpenAI
This AI-102 practice question tests your understanding of implement generative ai solutions. 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.
You are using Azure OpenAI to generate product descriptions. You notice that the descriptions are often too similar to each other. Which parameter should you adjust to increase diversity?
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
✓
Increase the temperature value.
Increasing the temperature parameter makes the model more creative by raising the probability of sampling lower-probability tokens, which increases diversity in the generated text. A higher temperature (e.g., 0.9) flattens the probability distribution, so the model is less likely to always pick the most probable next word, resulting in more varied outputs.
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.
- ✓
Increase the temperature value.
Why this is correct
Higher temperature increases randomness, leading to more diverse outputs.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Decrease the top_p value.
Why it's wrong here
Lower top_p reduces the pool of tokens considered, decreasing diversity.
- ✗
Increase the max_tokens value.
Why it's wrong here
max_tokens affects length, not diversity.
- ✗
Increase the frequency_penalty value.
Why it's wrong here
Frequency penalty reduces repetition but does not directly increase diversity.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Microsoft often tests the distinction between temperature (which controls randomness/creativity) and frequency_penalty (which controls repetition), leading candidates to mistakenly choose frequency_penalty when the question asks for diversity in content rather than just avoiding repetition.
Detailed technical explanation
How to think about this question
Temperature works by scaling the logits (raw scores) before applying the softmax function: logits = logits / temperature. A temperature of 1.0 leaves the distribution unchanged; values above 1.0 (e.g., 1.5) make the distribution more uniform, increasing diversity, while values below 1.0 (e.g., 0.2) sharpen the distribution, making outputs more deterministic. In practice, for creative tasks like product descriptions, a temperature between 0.7 and 0.9 is often used, while for factual or code generation, lower values (0.1–0.3) are preferred to reduce hallucination.
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.
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FAQ
Questions learners often ask
What does this AI-102 question test?
Implement generative AI solutions — This question tests Implement generative AI solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the temperature value. — Increasing the temperature parameter makes the model more creative by raising the probability of sampling lower-probability tokens, which increases diversity in the generated text. A higher temperature (e.g., 0.9) flattens the probability distribution, so the model is less likely to always pick the most probable next word, resulting in more varied outputs.
What should I do if I get this AI-102 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
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
2 more ways this is tested on AI-102
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 wants to use Azure OpenAI to generate text from a prompt. Which parameter controls the diversity of the generated output?
easy- A.presence_penalty
- B.frequency_penalty
- ✓ C.temperature
- D.max_tokens
Why C: Temperature is the parameter that directly controls the randomness or diversity of the generated output by scaling the logits before applying the softmax function. A higher temperature (e.g., 1.0) increases the probability of less likely tokens, producing more creative and varied responses, while a lower temperature (e.g., 0.1) makes the output more deterministic and focused.
Variation 2. You are developing a generative AI solution that uses Azure OpenAI Service. You need to control the creativity of the generated responses. Which parameter should you adjust?
easy- A.top_p
- B.max_tokens
- ✓ C.temperature
- D.frequency_penalty
Why C: The temperature parameter directly controls the randomness of token selection in the model's output. Lower values (e.g., 0.2) make the model more deterministic and focused, while higher values (e.g., 0.8) increase creativity and variability. This is the primary parameter for adjusting creativity in Azure OpenAI Service.
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Last reviewed: Jun 30, 2026
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