1Z0-1127-25 Using OCI Generative AI Service Practice Question
A data scientist is fine-tuning a model on OCI Generative AI to generate code comments. They use a dataset of 10,000 examples. After fine-tuning, the model generates comments that are too similar to the training data and lack generalization. What is the most likely cause?
⚠ Common exam trap
Watch out — candidates often confuse overfitting (caused by too many epochs) with underfitting (caused by insufficient data or low learning rate), leading them to incorrectly select option B or D.
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
✓
Too many training epochs.
When a fine-tuned model generates outputs that are too similar to the training data and lack generalization, it is a classic sign of overfitting. Overfitting occurs when the model is trained for too many epochs, causing it to memorize the training examples rather than learning the underlying patterns. In OCI Generative AI, the fine-tuning process adjusts model weights iteratively, and excessive epochs lead to poor performance on unseen data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Incorrect tokenizer.
Why it's wrong here
Tokenizer mismatch would cause encoding errors, not over-similarity.
- ✗
Insufficient training data.
Why it's wrong here
10,000 examples are typically sufficient; the issue is overfitting, not data size.
- ✓
Too many training epochs.
Why this is correct
Excessive epochs cause the model to memorize training data, reducing generalization.
- ✗
Too high learning rate.
Why it's wrong here
High learning rate leads to unstable training, not memorization.
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