- A
500
Why wrong: 500 may be borderline but 1000 is the stated minimum.
- B
1000
Cohere's documentation states a minimum of 1000 examples.
- C
5000
Why wrong: 5000 is more than necessary; the question asks for minimum.
- D
100
Why wrong: 100 examples are too few for fine-tuning to generalize.
Question 891 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.
A data scientist is fine-tuning a Cohere model on OCI Generative AI service for a custom classification task. They have a dataset of 1000 labeled examples. What is the minimum recommended dataset size for fine-tuning?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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
✓
1000
Cohere models on OCI Generative AI require a minimum of 1000 labeled examples for fine-tuning to ensure sufficient signal for learning task-specific patterns without overfitting. This threshold is documented in OCI's fine-tuning requirements and applies to custom classification tasks.
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.
- ✗
500
Why it's wrong here
500 may be borderline but 1000 is the stated minimum.
- ✓
1000
Why this is correct
Cohere's documentation states a minimum of 1000 examples.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
5000
Why it's wrong here
5000 is more than necessary; the question asks for minimum.
- ✗
100
Why it's wrong here
100 examples are too few for fine-tuning to generalize.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may assume a lower number like 500 is sufficient based on general machine learning heuristics, but OCI's specific fine-tuning documentation explicitly sets 1000 as the minimum, and Oracle tests this exact documented value.
Detailed technical explanation
How to think about this question
Fine-tuning adjusts the model's weights using labeled data, and Cohere's architecture requires enough examples to stabilize gradient updates across its attention layers. With fewer than 1000 examples, the model may memorize noise rather than learn meaningful classification boundaries, especially for tasks with high class imbalance. In practice, OCI recommends at least 100 examples per class, so for a binary classification task, 200 examples would be insufficient, reinforcing the 1000 total minimum.
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.
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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: 1000 — Cohere models on OCI Generative AI require a minimum of 1000 labeled examples for fine-tuning to ensure sufficient signal for learning task-specific patterns without overfitting. This threshold is documented in OCI's fine-tuning requirements and applies to custom classification tasks.
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: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 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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