Generative AI Leader Fundamentals of Generative AI Practice Question
A marketing team is using a generative AI model to create ad copy. They want to ensure the output is creative and diverse, but also relevant to the product. They decide to adjust the model's parameters. Which parameter should they increase to make the output more diverse?
⚠ Common exam trap
A common mix-up: candidates confuse parameters that control output length or sampling cutoffs with the one that directly scales randomness, which is temperature.
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 is the parameter that directly controls the randomness of the model's output. Increasing temperature makes the output more diverse and creative, which is desired for ad copy generation. While top-k and top-p also influence sampling, temperature is the primary knob for adjusting diversity. Max output tokens only controls length.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Top-k
Why it's wrong here
Top-k limits the model's choices to the k most probable next words. Decreasing top-k makes the output more focused and less diverse. Increasing top-k allows more words but still truncates the distribution. It is not the primary parameter for controlling diversity; temperature is more direct.
- ✓
Temperature
Why this is correct
Temperature controls the randomness of the model's predictions. A higher temperature increases diversity by making the probability distribution flatter, so less likely words are chosen more often. This leads to more creative and varied outputs. For ad copy, increasing temperature can help generate more diverse ideas while still being relevant if the prompt is clear.
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Top-p
Why it's wrong here
Top-p (nucleus sampling) selects from the smallest set of words whose cumulative probability exceeds p. A higher top-p includes more words, potentially increasing diversity, but it is often used in conjunction with temperature. However, temperature is the parameter that directly scales the logits and is the standard answer for increasing diversity.
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Max output tokens
Why it's wrong here
Max output tokens sets the maximum length of the generated text. It does not affect the diversity or creativity of the content. Increasing it allows longer outputs but does not change the randomness of word selection. This parameter is about length control, not diversity.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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