Generative AI Leader Fundamentals of Generative AI Practice Question
A software team is using a generative AI model to write code snippets. They want to control the model's creativity and ensure it produces consistent, deterministic output for the same prompt. Which parameter should they adjust?
⚠ Common exam trap
The trap here is thinking that top-p or top-k alone can produce fully deterministic output, when only temperature zero does.
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 controls the randomness of token selection. Setting it to zero makes the model choose the highest-probability token each time, yielding deterministic output for the same prompt. Top-p, top-k, and max tokens do not provide the same level of determinism.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Top-p (nucleus sampling)
Why it's wrong here
Top-p controls the cumulative probability threshold for token selection, but it still allows randomness. It does not guarantee deterministic output. While lower top-p reduces diversity, temperature is the primary parameter for determinism when set to zero.
- ✗
Top-k sampling
Why it's wrong here
Top-k restricts sampling to the k most likely tokens, but it still involves random selection among them. It reduces diversity but does not guarantee identical outputs. Temperature set to zero is the correct way to achieve determinism.
- ✗
Maximum output tokens
Why it's wrong here
Maximum output tokens limits the length of the response but does not affect randomness or determinism. It is useful for controlling cost and truncation, but it will not make outputs consistent across runs. This parameter is unrelated to creativity.
- ✓
Temperature
Why this is correct
Setting temperature to zero makes the model select the most likely next token at each step, producing deterministic output for the same prompt. This is ideal for code generation where consistency is important. Temperature directly controls randomness, making it the correct choice.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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