Generative AI Leader Fundamentals of Generative AI Practice Question
A marketing team wants to generate product descriptions from a short bullet list of features. They need the model to produce creative, varied phrasing rather than a single deterministic output, while keeping the text grammatically correct. Which Vertex AI generative AI parameter should they adjust to introduce randomness into the model's word choices?
⚠ Common exam trap
Watch out — candidates often confuse sampling-shaping parameters like Top-K and Top-P with the parameter that actually controls 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 sampling parameter that scales the model's predicted token distribution, making outputs more random and creative when increased. Marketing copy benefits from this controlled variability because it produces distinct phrasings from the same feature list. Other parameters such as Top-K, Top-P, and maximum output tokens influence candidate selection or length but do not by themselves introduce the randomness the team wants.
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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Maximum output tokens
Why it's wrong here
Maximum output tokens caps how many tokens the model may generate before stopping. It governs response length and cost, but it has no effect on how the model chooses among candidate words. Setting a higher or lower cap will not make descriptions more creative or varied; it only changes whether the text is cut off. It is irrelevant to the randomness requirement.
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Top-P
Why it's wrong here
Top-P, or nucleus sampling, keeps the smallest set of tokens whose cumulative probability exceeds P, then samples from that set. It shapes diversity by trimming the tail of unlikely tokens but does not directly tune how flat or peaked the distribution is. While it affects variety, the scenario asks for the parameter that introduces randomness itself, which is temperature, not the cumulative cutoff.
- ✓
Temperature
Why this is correct
Temperature controls the randomness of token selection by scaling the model's output logits before sampling. Raising temperature flattens the probability distribution, giving lower-probability words a larger chance of being chosen, which yields more varied and creative phrasing. For a marketing use case that explicitly wants diverse outputs rather than one deterministic answer, temperature is the direct and intended control.
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Top-K
Why it's wrong here
Top-K restricts sampling to the K most likely tokens at each step, which can improve coherence but does not by itself increase randomness across the whole distribution the way temperature does. It only truncates the candidate pool; with a small K, output becomes more predictable, and with a large K it still leaves probabilities unmodified. It is not the parameter that introduces controlled randomness here.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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