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Generative AI Leader Practice Question: A developer is using the Gemini API to generate…
A developer is using the Gemini API to generate marketing copy. They want the output to be diverse and creative but still relevant to the topic. Which THREE parameter adjustments would help achieve this? (Choose 3)
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
✓
Increase temperature to 0.9
Increasing temperature to 0.9 (option A) is correct because higher temperature flattens the probability distribution, making the model more likely to pick less probable tokens and thus produce more diverse, creative text while still grounded in the prompt. Increasing top-k to 50 (option B) is correct because a larger top-k widens the candidate pool from which the next token is sampled, allowing more varied word choices and greater output diversity. Increasing top-p to 0.95 (option E) is correct because a higher nucleus sampling threshold includes more tokens cumulatively comprising 95% of the probability mass, which increases randomness and creativity while excluding only the least likely tokens. Decreasing temperature to 0.1 (option C) is not correct because low temperature makes the model nearly deterministic and conservative, reducing diversity. Decreasing top-k to 10 (option D) is not correct because a smaller top-k restricts sampling to fewer high-probability tokens, which narrows creativity rather than enhancing it.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Increase temperature to 0.9
Why this is correct
Raising temperature to 0.9 flattens the softmax probability distribution, so lower-probability tokens are sampled more often. This directly satisfies the stem's demand for diverse, creative marketing copy while the prompt's topic framing keeps output relevant.
- ✓
Increase top-k to 50
Why this is correct
Increasing top-k to 50 widens the candidate pool from which each token is sampled, rather than restricting sampling to the single most probable token. This raises lexical variety, satisfying the creativity requirement, while the topic-conditioned prompt still anchors relevance.
- ✗
Decrease temperature to 0.1
Why it's wrong here
Lowering temperature to 0.1 sharpens the probability distribution toward the highest-likelihood tokens, producing repetitive, deterministic output rather than the diverse copy requested. It is tempting because low temperature is the standard setting for factual, consistent tasks such as classification or extraction, where creativity must be suppressed.
- ✗
Decrease top-k to 10
Why it's wrong here
Lowering top-k restricts sampling to the ten highest-probability tokens, reducing diversity and pushing output toward predictable phrasing. It tempts because top-k controls randomness, and decreasing it would be correct when the goal is focused, deterministic responses rather than creative variation.
- ✓
Increase top-p to 0.95
Why this is correct
Raising top-p to 0.95 applies nucleus sampling, retaining the smallest token set whose cumulative probability reaches 95%. This admits more varied vocabulary than a low top-p cutoff, delivering the diversity the stem requires without abandoning topical relevance.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This Generative AI Leader practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Generative AI Leader exam.