1Z0-1127 Using OCI Generative AI Service Practice Question
This 1Z0-1127 practice question tests your understanding of using oci generative ai service. 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.
Exhibit
Refer to the exhibit.
Error:
{
"code": "InvalidParameter",
"message": "The parameter 'top_p' should be between 0 and 1.",
"status": 400
}
A developer makes an API call to generate text with top_p=1.5. What is the correct way to fix this error?
Exhibit
Refer to the exhibit.
Error:
{
"code": "InvalidParameter",
"message": "The parameter 'top_p' should be between 0 and 1.",
"status": 400
}
A
Remove the top_p parameter from the request
Why wrong: Removing it uses default, which is valid, but if they need top_p, adjust value.
B
Increase the temperature parameter to compensate
Why wrong: Temperature does not affect top_p validity.
C
Set top_p to a value between 0 and 1, e.g., 0.9
Correcting the value to within the allowed range fixes the error.
D
Use the top_k parameter instead
Why wrong: Changing parameters avoids the error but does not fix the invalid top_p value.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Set top_p to a value between 0 and 1, e.g., 0.9
The `top_p` parameter, also known as nucleus sampling, must be a probability value between 0 and 1. Setting it to 1.5 is invalid because it exceeds the allowed range, which would cause the API to reject the request. The correct fix is to set `top_p` to a valid value such as 0.9, which restricts token selection to the smallest set whose cumulative probability exceeds that threshold.
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.
✗
Remove the top_p parameter from the request
Why it's wrong here
Removing it uses default, which is valid, but if they need top_p, adjust value.
✗
Increase the temperature parameter to compensate
Why it's wrong here
Temperature does not affect top_p validity.
✓
Set top_p to a value between 0 and 1, e.g., 0.9
Why this is correct
Correcting the value to within the allowed range fixes the error.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use the top_k parameter instead
Why it's wrong here
Changing parameters avoids the error but does not fix the invalid top_p value.
Common exam traps
Common exam trap: answer the scenario, not the keyword
OCI often tests the misconception that `top_p` can be any positive number, similar to `temperature`, when in fact it is a probability threshold strictly bounded between 0 and 1.
Detailed technical explanation
How to think about this question
In OCI Generative AI, `top_p` (nucleus sampling) works by considering only the tokens whose cumulative probability mass exceeds the threshold p, then sampling from that subset. A value of 1.5 is invalid because cumulative probability cannot exceed 1.0; the API will return a validation error. In practice, setting `top_p` too low (e.g., 0.1) can make output overly deterministic, while values near 1.0 (e.g., 0.9) provide a good balance between creativity and coherence.
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.
Using OCI Generative AI Service — This question tests Using OCI Generative AI Service — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Set top_p to a value between 0 and 1, e.g., 0.9 — The `top_p` parameter, also known as nucleus sampling, must be a probability value between 0 and 1. Setting it to 1.5 is invalid because it exceeds the allowed range, which would cause the API to reject the request. The correct fix is to set `top_p` to a valid value such as 0.9, which restricts token selection to the smallest set whose cumulative probability exceeds that threshold.
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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Question Discussion
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