- A
Increase the frequency penalty parameter to 1.0.
Frequency penalty specifically reduces the model's tendency to repeat tokens, improving lexical diversity.
- B
Decrease the max output tokens to 50.
Why wrong: Shorter output does not solve the repetition of phrases within the allowed length.
- C
Increase the temperature parameter to 1.5.
Why wrong: Higher temperature can reduce repetition but also introduces more randomness and potential incoherence.
- D
Set the top-p value to a very small number like 0.1.
Why wrong: Small top-p narrows the vocabulary, potentially increasing repetition of common words.
Question 675 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 content generation model for e-commerce product descriptions repeats the same phrases across multiple descriptions (e.g., 'high-quality', 'best-in-class'). The team wants more varied and engaging output. 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 parameter to 1.0.
Increasing the frequency penalty to 1.0 penalizes tokens that have already appeared in the generated text, directly reducing repetition of phrases like 'high-quality' and 'best-in-class'. This encourages the model to use more diverse vocabulary and sentence structures, leading to varied and engaging product descriptions.
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 frequency penalty parameter to 1.0.
Why this is correct
Frequency penalty specifically reduces the model's tendency to repeat tokens, improving lexical diversity.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Decrease the max output tokens to 50.
Why it's wrong here
Shorter output does not solve the repetition of phrases within the allowed length.
- ✗
Increase the temperature parameter to 1.5.
Why it's wrong here
Higher temperature can reduce repetition but also introduces more randomness and potential incoherence.
- ✗
Set the top-p value to a very small number like 0.1.
Why it's wrong here
Small top-p narrows the vocabulary, potentially increasing repetition of common words.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between frequency penalty and temperature, where candidates mistakenly increase temperature to add variety, not realizing that temperature increases randomness and can break coherence, while frequency penalty directly targets repetition without sacrificing quality.
Trap categories for this question
Keyword trap
Shorter output does not solve the repetition of phrases within the allowed length.
Command / output trap
Shorter output does not solve the repetition of phrases within the allowed length.
Detailed technical explanation
How to think about this question
The frequency penalty works by subtracting a fixed penalty (proportional to the penalty parameter) from the log-probability of each token each time it has been generated so far, effectively lowering its chance of being chosen again. This is distinct from the presence penalty, which applies a one-time penalty regardless of frequency. In practice, a frequency penalty of 1.0 is a strong setting that can significantly reduce n-gram repetition without sacrificing coherence, making it ideal for e-commerce descriptions where brand consistency is still needed.
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
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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 parameter to 1.0. — Increasing the frequency penalty to 1.0 penalizes tokens that have already appeared in the generated text, directly reducing repetition of phrases like 'high-quality' and 'best-in-class'. This encourages the model to use more diverse vocabulary and sentence structures, leading to varied and engaging product descriptions.
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
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