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?
Increase the temperature value.
Higher temperature increases randomness, leading to more diverse outputs.
Decrease the top_p value.
Why wrong: Lower top_p reduces the pool of tokens considered, decreasing diversity.
Increase the max_tokens value.
Why wrong: max_tokens affects length, not diversity.
Increase the frequency_penalty value.
Why wrong: Frequency penalty reduces repetition but does not directly increase diversity.
Answer choices
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
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
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
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.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Implement generative AI solutions — This question tests Implement generative AI solutions — Read the scenario before looking for a memorised answer..
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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