1Z0-1127-25 Using OCI Generative AI Service Practice Question
Which TWO of the following are required to fine-tune a model using OCI Generative AI Service?
⚠ Common exam trap
Oracle often tests the misconception that you need a dedicated AI cluster or an API key for every operation, but OCI Generative AI Service abstracts infrastructure management and supports multiple authentication methods, making those options distractors.
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
✓
A training dataset in the required format
A is correct because fine-tuning a model in OCI Generative AI Service requires a training dataset in the required format (JSONL with prompt-completion pairs) to provide the task-specific examples that adjust the model's weights. B is correct because you must specify the base model identifier (e.g., 'cohere.command-light-14-07-2024') to indicate which pre-trained model to fine-tune, as the service uses this to load the correct architecture and initial parameters.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
A training dataset in the required format
Why this is correct
Training data is essential for fine-tuning.
- ✓
The base model identifier
Why this is correct
You must specify which base model to fine-tune.
- ✗
A compartment with sufficient quota
Why it's wrong here
Quota is needed but not a technical requirement; it's an administrative one.
- ✗
An OCI API key
Why it's wrong here
Authentication can use other methods like resource principal.
- ✗
A dedicated AI cluster
Why it's wrong here
Dedicated cluster is optional; fine-tuning can use shared infrastructure.
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