Generative AI Leader Fundamentals of Generative AI Practice Question
A marketing team is using a generative AI model to create ad copy. They notice that the outputs are often too creative and sometimes include exaggerated claims. They want to reduce creativity and make the outputs more predictable and factual. Which parameter should they adjust?
⚠ Common exam trap
Watch out — candidates often confuse temperature with other sampling parameters like top-p or top-k, which also affect randomness but are not the primary control for creativity.
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 scales the logits before softmax, directly affecting the probability distribution of the next token. A lower temperature makes the distribution sharper, so the model chooses more likely tokens, resulting in more predictable and less creative outputs. This aligns with the team's goal of reducing exaggerated claims and increasing factual consistency.
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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Max output tokens
Why it's wrong here
Max output tokens limits the length of the generated text but does not affect creativity or factual accuracy. Adjusting this parameter would only truncate the output, potentially cutting off important information, and would not address the issue of exaggerated claims or unpredictability.
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Top-p
Why it's wrong here
Top-p (nucleus sampling) controls the diversity of outputs by limiting the token selection to a cumulative probability threshold. Lowering top-p can reduce randomness, but it is not the primary parameter for controlling creativity; temperature is more directly responsible for the level of randomness and creativity in generated text.
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Top-k
Why it's wrong here
Top-k sampling restricts the model to the k most likely tokens at each step. While it can reduce randomness, it is less commonly used than temperature for controlling creativity. Lowering top-k might help, but temperature is the primary and most effective parameter for making outputs more deterministic and less creative.
- ✓
Temperature
Why this is correct
Temperature directly controls the randomness of the model's predictions. Lowering the temperature makes the model more deterministic and less creative, which is exactly what the team needs to reduce exaggerated claims and make outputs more predictable and factual. It is the most straightforward parameter to adjust for this purpose.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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