AI-102 Implement generative AI solutions Practice Question
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?
⚠ Common exam trap
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.
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.
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.
- ✗
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.
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