- A
Top_p = 0.1, frequency_penalty = 0.5
Why wrong: Top_p = 0.1 severely limits the pool of possible tokens, often making output repetitive despite the penalty.
- B
Top_p = 0.9, frequency_penalty = 0.5
This combination applies a gentle penalty on repeated tokens while keeping token selection diverse, effectively reducing repetition.
- C
Top_p = 0.9, frequency_penalty = 0.0
Why wrong: Without any frequency penalty, the model has no incentive to avoid repetition.
- D
Top_p = 1.0, frequency_penalty = 0.0
Why wrong: Top_p = 1.0 includes all tokens (no nucleus sampling) and no penalty, so repetition is likely.
Question 195 of 991
1Z0-1127 Fundamentals of Large Language Models Practice Question
This 1Z0-1127 practice question tests your understanding of fundamentals of large language models. 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.
During inference with OCI Generative AI, you notice that the model is generating repetitive phrases. Which combination of parameters can help reduce repetition?
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
✓
Top_p = 0.9, frequency_penalty = 0.5
Option B is correct because a high Top_p value (0.9) allows the model to consider a diverse set of tokens, reducing the chance of getting stuck in repetitive loops, while a positive frequency_penalty (0.5) actively penalizes tokens that have already been generated, discouraging the model from repeating the same phrases. Together, these parameters balance creativity and repetition suppression.
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.
- ✗
Top_p = 0.1, frequency_penalty = 0.5
Why it's wrong here
Top_p = 0.1 severely limits the pool of possible tokens, often making output repetitive despite the penalty.
- ✓
Top_p = 0.9, frequency_penalty = 0.5
Why this is correct
This combination applies a gentle penalty on repeated tokens while keeping token selection diverse, effectively reducing repetition.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Top_p = 0.9, frequency_penalty = 0.0
Why it's wrong here
Without any frequency penalty, the model has no incentive to avoid repetition.
- ✗
Top_p = 1.0, frequency_penalty = 0.0
Why it's wrong here
Top_p = 1.0 includes all tokens (no nucleus sampling) and no penalty, so repetition is likely.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Oracle often tests the misconception that lowering Top_p (making it more restrictive) reduces repetition, when in fact it can worsen repetition by limiting the model to only the most probable tokens, which are often the same ones already used.
Trap categories for this question
Command / output trap
Top_p = 0.1 severely limits the pool of possible tokens, often making output repetitive despite the penalty.
Detailed technical explanation
How to think about this question
Top_p (nucleus sampling) dynamically selects the smallest set of tokens whose cumulative probability exceeds the threshold, so a value of 0.9 includes a wide range of plausible tokens, while a value of 0.1 severely truncates the vocabulary. Frequency_penalty works by subtracting a fixed penalty (proportional to the number of times a token has appeared) from the token's logit before applying softmax, making repeated tokens less likely to be chosen. In practice, tuning these parameters is critical for tasks like story generation or dialogue, where repetition can make output seem unnatural or broken.
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 practitioner preparing for the 1Z0-1127 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this 1Z0-1127 question test?
Fundamentals of Large Language Models — This question tests Fundamentals of Large Language Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Top_p = 0.9, frequency_penalty = 0.5 — Option B is correct because a high Top_p value (0.9) allows the model to consider a diverse set of tokens, reducing the chance of getting stuck in repetitive loops, while a positive frequency_penalty (0.5) actively penalizes tokens that have already been generated, discouraging the model from repeating the same phrases. Together, these parameters balance creativity and repetition suppression.
What should I do if I get this 1Z0-1127 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: Jun 30, 2026
This 1Z0-1127 practice question is part of Courseiva's free Oracle 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 1Z0-1127 exam.
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