- A
Decrease the top-k value.
Why wrong: Top-k affects vocabulary diversity, not output length.
- B
Increase the max output tokens.
Why wrong: This would allow even longer descriptions, opposite of desired.
- C
Decrease the temperature.
Why wrong: Lower temperature reduces creativity but doesn't directly reduce verbosity.
- D
Increase the frequency penalty.
Frequency penalty reduces repetitive phrases, encouraging conciseness.
Question 707 of 997
Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
This Generative AI Leader practice question tests your understanding of techniques to improve generative ai model output. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 data scientist is using a large language model to generate product descriptions. The descriptions are often too verbose. Which parameter adjustment is most appropriate?
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 frequency penalty.
Increasing the frequency penalty reduces the likelihood of the model repeating the same phrases or ideas, which directly addresses verbosity by discouraging repetitive or overly detailed descriptions. This parameter penalizes tokens that have already appeared in the generated text, promoting more concise and varied output. Other adjustments like temperature or top-k affect randomness and diversity but do not specifically target repetition or length.
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.
- ✗
Decrease the top-k value.
Why it's wrong here
Top-k affects vocabulary diversity, not output length.
- ✗
Increase the max output tokens.
Why it's wrong here
This would allow even longer descriptions, opposite of desired.
- ✗
Decrease the temperature.
Why it's wrong here
Lower temperature reduces creativity but doesn't directly reduce verbosity.
- ✓
Increase the frequency penalty.
Why this is correct
Frequency penalty reduces repetitive phrases, encouraging conciseness.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between parameters that control randomness (temperature, top-k) versus those that control repetition (frequency penalty, presence penalty), and the trap here is that candidates confuse 'less verbose' with 'less random' and incorrectly choose temperature or top-k adjustments.
Trap categories for this question
Command / output trap
Top-k affects vocabulary diversity, not output length.
Detailed technical explanation
How to think about this question
The frequency penalty operates by adding a penalty proportional to the number of times a token has already appeared in the sequence, effectively reducing its logit score during sampling. This is distinct from the presence penalty, which applies a fixed penalty once per token regardless of frequency. In practice, a frequency penalty of 0.5 to 1.0 can significantly reduce repetitive loops in product descriptions without forcing truncation, making it ideal for tasks requiring concise output.
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.
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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Techniques to Improve Generative AI Model Output — This question tests Techniques to Improve Generative AI Model Output — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the frequency penalty. — Increasing the frequency penalty reduces the likelihood of the model repeating the same phrases or ideas, which directly addresses verbosity by discouraging repetitive or overly detailed descriptions. This parameter penalizes tokens that have already appeared in the generated text, promoting more concise and varied output. Other adjustments like temperature or top-k affect randomness and diversity but do not specifically target repetition or length.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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.
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