1Z0-1127-25 OCI Generative AI Service Practice Question
A team is fine-tuning a Llama 3 model using OCI Generative AI. The training dataset contains 10,000 prompt-completion pairs in JSONL format. After submitting the fine-tuning job, it fails with a 'Data validation error'. What is the most likely cause?
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
✓
The JSONL file uses 'input' and 'output' as key names instead of 'prompt' and 'completion'
OCI fine-tuning requires a specific JSONL format with 'prompt' and 'completion' keys. If the keys are named differently (e.g., 'input'/'output'), validation fails. Model units, compartment, and base model are not typically validation issues.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The base model selected does not support fine-tuning
Why it's wrong here
If the model doesn't support fine-tuning, the job would fail earlier with a different error, not during data validation.
- ✗
The dataset is in the wrong compartment
Why it's wrong here
Compartment misconfiguration causes authorization errors, not data validation errors.
- ✗
The fine-tuning job does not have enough model units allocated
Why it's wrong here
Insufficient model units would cause resource errors, not data validation errors.
- ✓
The JSONL file uses 'input' and 'output' as key names instead of 'prompt' and 'completion'
Why this is correct
OCI Generative AI expects exact key names 'prompt' and 'completion' in the JSONL training file; using alternate keys causes a data validation error.
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