The correct answer is to increase the frequency penalty parameter to 0.5, as this directly counters repetitive outputs by applying a proportional penalty to tokens that have already appeared in the generated text. When a model produces repetitive phrases, it is often because the default frequency penalty of 0.0 places no restriction on token reuse, allowing the same words or structures to be selected repeatedly. By raising this value, the inference endpoint reduces the probability of sampling already-used tokens, forcing the model to explore more diverse vocabulary and improving overall coherence. On the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 exam, this question tests your understanding of how sampling parameters control output quality, with a common trap being to confuse frequency penalty with presence penalty—the former targets repetition frequency, while the latter penalizes any token that has appeared at least once. A useful memory tip is to think of “frequency” as “frequent repeats,” so increasing the penalty reduces the frequency of those repeats.
1Z0-1127 Deploying and Managing Generative AI on OCI Practice Question
This 1Z0-1127 practice question tests your understanding of deploying and managing generative ai on oci. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 company has deployed a generative AI model on OCI to generate product descriptions. After a recent update, the model started producing outputs with repetitive phrases and poor coherence. The inference endpoint is configured with default parameters. Which single parameter adjustment is most likely to improve output quality?
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 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 0.5
The correct answer is B because increasing the frequency penalty reduces the likelihood of the model repeating the same phrases, directly addressing the repetitive outputs. The frequency penalty subtracts a proportional penalty from tokens that have already appeared, discouraging repetition and improving coherence. Default parameters often have no frequency penalty (0.0), so a small positive value like 0.5 can significantly enhance output diversity.
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 max-tokens parameter to 512
Why it's wrong here
Increasing max tokens only allows longer outputs, not fixing repetition.
✓
Increase the frequency penalty parameter to 0.5
Why this is correct
Frequency penalty reduces repeated tokens, directly improving repetitive output.
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.
✗
Increase the temperature parameter to 1.5
Why it's wrong here
Increasing temperature increases randomness, which may worsen coherence and does not directly address repetition.
✗
Decrease the top-p parameter to 0.8
Why it's wrong here
Decreasing top-p narrows token selection but does not penalize repetition.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse frequency penalty with temperature or top-p, assuming that increasing randomness (temperature) or narrowing token selection (top-p) will fix repetition, when in fact those parameters address different aspects of output diversity and coherence.
Trap categories for this question
Command / output trap
Increasing max tokens only allows longer outputs, not fixing repetition.
Detailed technical explanation
How to think about this question
The frequency penalty works by adding a penalty proportional to the number of times a token has already been generated in the sequence, scaled by the frequency_penalty parameter. This is applied during logit calculation before sampling, effectively lowering the probability of repeated tokens. In contrast, the presence penalty (often confused with frequency penalty) applies a flat penalty regardless of frequency, so frequency penalty is more targeted for repetitive phrase issues.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Deploying and Managing Generative AI on OCI — This question tests Deploying and Managing Generative AI on OCI — 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 0.5 — The correct answer is B because increasing the frequency penalty reduces the likelihood of the model repeating the same phrases, directly addressing the repetitive outputs. The frequency penalty subtracts a proportional penalty from tokens that have already appeared, discouraging repetition and improving coherence. Default parameters often have no frequency penalty (0.0), so a small positive value like 0.5 can significantly enhance output diversity.
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.
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.
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Question Discussion
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