1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
Which TWO factors should be considered when selecting a base model for fine-tuning on OCI Generative AI service?
⚠ Common exam trap
Oracle often tests the misconception that technical details like training framework or dataset size are relevant, when in fact the exam focuses on operational and legal factors (size/license) that directly affect deployment and compliance in OCI's managed service.
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 model's size and number of parameters
When selecting a base model for fine-tuning on OCI Generative AI service, the model's size and number of parameters (B) directly impact computational cost, training time, and the model's capacity to learn from your dataset. The model's license and terms of use (C) are critical because commercial use, redistribution, and fine-tuning rights vary per model (e.g., Llama 2 vs. GPT-based models), and violating these can lead to legal or compliance 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 model's training dataset size
Why it's wrong here
Base model is pre-trained; you add your own dataset for fine-tuning.
- ✓
The model's size and number of parameters
Why this is correct
Larger models consume more resources and cost more to serve.
- ✓
The model's license and terms of use
Why this is correct
Must comply with licensing for commercial use.
- ✗
The model's training framework (PyTorch vs TensorFlow)
Why it's wrong here
Fine-tuning uses OCI managed infrastructure; framework is abstracted.
- ✗
The model's built-in features like content filtering
Why it's wrong here
Content filtering can be added post-deployment; not a base model selection factor.
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