1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
A team uses OCI Generative AI’s fine-tuning capability to adapt a base model. After fine-tuning, they evaluate the model but see degraded performance on certain edge cases. What is the most likely cause?
⚠ Common exam trap
Oracle often tests the distinction between overfitting and underfitting by presenting a scenario where performance is good on training data but poor on unseen data, leading candidates to incorrectly blame a high learning rate or insufficient epochs.
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
✓
Overfitting on the training data
Fine-tuning adapts a base model to a specific dataset, but if the training data is too narrow or the model is trained for too many epochs, it can memorize the training examples rather than learning generalizable patterns. This overfitting causes the model to perform well on training-like inputs but poorly on edge cases that deviate from the training distribution. In OCI Generative AI, overfitting is a common pitfall when fine-tuning hyperparameters like the number of epochs or learning rate are not properly validated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Overfitting on the training data
Why this is correct
Overfitting leads to poor generalization, especially on edge cases not seen during training.
- ✗
Validation data leakage
Why it's wrong here
Data leakage would inflate validation metrics, not degrade edge cases.
- ✗
Learning rate too high
Why it's wrong here
A high learning rate might cause training instability, not specifically poor edge case performance.
- ✗
Insufficient training epochs
Why it's wrong here
This would likely cause underfitting, not degraded performance on edge cases alone.
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