- A
frequency_penalty
Why wrong: Frequency penalty reduces the likelihood of tokens that have already appeared.
- B
top_k
Why wrong: Top_k limits the next token selection to the K most likely tokens.
- C
temperature
Higher temperature increases randomness by scaling logits before softmax.
- D
top_p
Why wrong: Top_p (nucleus sampling) selects from the smallest set of tokens whose cumulative probability exceeds p.
Question 457 of 991
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.
Which OCI Generative AI parameter controls the diversity of generated text by increasing the probability of less likely tokens?
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
✓
temperature
The temperature parameter in OCI Generative AI controls the randomness of token selection by scaling the logits before applying the softmax function. A higher temperature (e.g., > 1.0) increases the probability of less likely tokens, making the output more diverse and creative, while a lower temperature (e.g., < 1.0) sharpens the distribution toward the most likely tokens, producing more deterministic and conservative text.
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.
- ✗
frequency_penalty
Why it's wrong here
Frequency penalty reduces the likelihood of tokens that have already appeared.
- ✗
top_k
Why it's wrong here
Top_k limits the next token selection to the K most likely tokens.
- ✓
temperature
Why this is correct
Higher temperature increases randomness by scaling logits before softmax.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
top_p
Why it's wrong here
Top_p (nucleus sampling) selects from the smallest set of tokens whose cumulative probability exceeds p.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The OCI Generative AI exam often tests the distinction between temperature and top_p/top_k by asking which parameter directly scales probabilities to favor less likely tokens, leading candidates to confuse sampling strategies (top_p/top_k) with the logit scaling mechanism (temperature).
Detailed technical explanation
How to think about this question
Under the hood, temperature modifies the logits (pre-softmax scores) by dividing them by the temperature value before softmax normalization. For example, a temperature of 2.0 flattens the probability distribution, giving lower-ranked tokens a relatively higher chance of being selected, while a temperature of 0.5 sharpens it, making the model nearly greedy. In OCI Generative AI, this parameter is critical for balancing creativity vs. coherence in use cases like story generation (high temperature) versus factual Q&A (low temperature).
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
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FAQ
Questions learners often ask
What does this 1Z0-1127 question test?
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: temperature — The temperature parameter in OCI Generative AI controls the randomness of token selection by scaling the logits before applying the softmax function. A higher temperature (e.g., > 1.0) increases the probability of less likely tokens, making the output more diverse and creative, while a lower temperature (e.g., < 1.0) sharpens the distribution toward the most likely tokens, producing more deterministic and conservative text.
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: Jul 4, 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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