Generative AI Leader Fundamentals of Generative AI Practice Question
A marketing team is using Vertex AI's text generation model to create product descriptions. They want to control the randomness of the output to ensure consistent, focused messaging for a new product line. Which parameter should they adjust to reduce randomness and make the output more deterministic?
⚠ Common exam trap
Candidates often confuse top-k or top-p with temperature; while they influence sampling, temperature is the primary control for randomness.
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. Lowering the temperature makes the model more likely to choose high-probability tokens, resulting in more deterministic and consistent text. This is exactly what the marketing team needs for focused product descriptions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Max output tokens
Why it's wrong here
Max output tokens limits the length of the generated text. It does not affect the randomness or creativity of the output. Setting a low value would truncate responses, but would not make them more consistent or focused in terms of content. It is unrelated to controlling randomness.
- ✓
Temperature
Why this is correct
Temperature controls the randomness of predictions by scaling the logits before applying softmax. A lower temperature (e.g., 0.2) makes the model more confident and deterministic, producing focused outputs. This directly addresses the need for consistent messaging. Higher temperatures increase diversity but reduce consistency.
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Top-k
Why it's wrong here
Top-k limits the next token selection to the k most likely tokens. While it can reduce randomness, it does not directly control the confidence of the model. A small k can make output more deterministic, but temperature is the primary parameter for adjusting randomness. Top-k alone may still produce varied outputs if temperature is high.
- ✗
Top-p
Why it's wrong here
Top-p (nucleus sampling) selects the smallest set of tokens whose cumulative probability exceeds p. It dynamically adjusts the number of tokens considered, which can balance diversity and coherence. However, it does not directly reduce randomness in the same way as lowering temperature. For deterministic output, temperature is more effective.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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