Quick Answer
The answer is 1000 labeled examples, which is the minimum recommended dataset size for fine-tuning Cohere models on OCI Generative AI. This threshold exists because a dataset of this size provides enough signal for the model to learn task-specific patterns for custom classification tasks while mitigating the risk of overfitting to noise. On the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 exam, this requirement tests your understanding of OCI’s documented fine-tuning constraints, often appearing as a direct numerical fact in scenario-based questions. A common trap is assuming smaller datasets—like 100 or 500 examples—are sufficient, but OCI explicitly mandates 1000 as the floor for reliable performance. Remember the mnemonic “One Thousand for One Task” to recall that fine-tuning a single custom classification task needs at least 1,000 labeled examples to achieve stable, generalizable results.
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.
500
Why wrong: 500 may be borderline but 1000 is the stated minimum.
1000
Cohere's documentation states a minimum of 1000 examples.
5000
Why wrong: 5000 is more than necessary; the question asks for minimum.
100
Why wrong: 100 examples are too few for fine-tuning to generalize.
Answer choices
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
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
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 Cisco tests this exact documented value.
Detailed technical explanation
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.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Using OCI Generative AI Service — This question tests Using OCI Generative AI Service — Read the scenario before looking for a memorised answer..
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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