The correct combination to reduce repetitive phrases during inference is Top_p = 0.9 and frequency_penalty = 0.5. This works because a high Top_p value of 0.9 allows the model to sample from a wider, more diverse set of tokens, preventing it from fixating on a narrow loop of likely words, while a positive frequency_penalty of 0.5 actively reduces the probability of tokens that have already appeared, directly suppressing repetition. On the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 exam, this question tests your understanding of how sampling parameters control output diversity versus determinism, often appearing as a scenario where the model is stuck in a cycle. A common trap is confusing Top_p with Top_k—remember that Top_p controls cumulative probability mass, not a fixed count. For a memory tip, think of Top_p as the "diversity dial" and frequency_penalty as the "repeat repeller"; together, they keep the model from falling into a rut.
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 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 small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
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.
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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