- A
Increase the top-k value from 20 to 50
Why wrong: Increasing top-k may add variety, but temperature has a more direct effect on randomness.
- B
Increase the temperature from 0.7 to 1.2
Higher temperature increases randomness, reducing repetitiveness.
- C
Decrease the temperature from 0.7 to 0.2
Why wrong: Lower temperature makes outputs more deterministic and less diverse.
- D
Decrease the top-p value from 0.9 to 0.5
Why wrong: Lower top-p restricts to more probable tokens, reducing diversity.
Question 13 of 997
Generative AI Leader Generative AI Concepts and Technologies Practice Question
This Generative AI Leader practice question tests your understanding of generative ai concepts and technologies. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A developer is using the Gemini API for text generation and finds that the outputs are too repetitive. Which parameter adjustment is most likely to increase output diversity?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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 the temperature from 0.7 to 1.2
Increasing the temperature parameter from 0.7 to 1.2 increases the randomness of token selection by scaling the logits before applying the softmax function, which flattens the probability distribution. This makes lower-probability tokens more likely to be chosen, directly reducing repetitiveness in generated text. In the Gemini API, temperature values above 1.0 amplify diversity, while values below 1.0 make outputs more deterministic and repetitive.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 the top-k value from 20 to 50
Why it's wrong here
Increasing top-k may add variety, but temperature has a more direct effect on randomness.
- ✓
Increase the temperature from 0.7 to 1.2
Why this is correct
Higher temperature increases randomness, reducing repetitiveness.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Decrease the temperature from 0.7 to 0.2
Why it's wrong here
Lower temperature makes outputs more deterministic and less diverse.
- ✗
Decrease the top-p value from 0.9 to 0.5
Why it's wrong here
Lower top-p restricts to more probable tokens, reducing diversity.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google often tests the misconception that increasing top-k or decreasing top-p increases diversity, when in fact both restrict the token pool and reduce randomness, while temperature is the direct parameter for controlling output variability.
Trap categories for this question
Command / output trap
Lower temperature makes outputs more deterministic and less diverse.
Detailed technical explanation
How to think about this question
Temperature works by dividing the logits (raw scores) by the temperature value before softmax; a temperature of 1.2 means logits are divided by 1.2, reducing the contrast between high and low scores, while a temperature of 0.2 amplifies differences. In practice, for creative tasks like story generation, a temperature of 0.8–1.2 is common, while for factual tasks like summarization, lower values (0.2–0.5) are preferred. The Gemini API also supports combined use of temperature, top-k, and top-p, but temperature is the primary control for overall randomness.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Generative AI Concepts and Technologies — This question tests Generative AI Concepts and Technologies — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the temperature from 0.7 to 1.2 — Increasing the temperature parameter from 0.7 to 1.2 increases the randomness of token selection by scaling the logits before applying the softmax function, which flattens the probability distribution. This makes lower-probability tokens more likely to be chosen, directly reducing repetitiveness in generated text. In the Gemini API, temperature values above 1.0 amplify diversity, while values below 1.0 make outputs more deterministic and repetitive.
What should I do if I get this Generative AI Leader question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jul 4, 2026
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