1Z0-1127-25 Fundamentals of Large Language Models Practice Question
A team is fine-tuning an LLM on OCI Generative AI for a domain-specific task. They have a dataset of 10,000 labeled examples. What is a best practice to avoid catastrophic forgetting during fine-tuning?
⚠ Common exam trap
Oracle often tests the misconception that fine-tuning should exclusively use the new dataset, whereas the best practice is to blend in general data to preserve prior knowledge.
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
✓
Include a small percentage of general-domain data in the training mix.
Catastrophic forgetting occurs when a fine-tuned model loses previously learned general knowledge. By including a small percentage (e.g., 5–10%) of general-domain data in the training mix, the model retains its broad capabilities while adapting to the new domain-specific task. This technique, often called 'replay' or 'experience replay,' is a standard practice in continual learning for LLMs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the learning rate to speed up adaptation.
Why it's wrong here
High learning rates can cause instability and forgetting.
- ✗
Use only the new domain-specific data for fine-tuning.
Why it's wrong here
Exclusive training on new data leads to forgetting general knowledge.
- ✗
Reduce the number of training epochs to the minimum.
Why it's wrong here
Too few epochs may not adapt the domain knowledge sufficiently.
- ✓
Include a small percentage of general-domain data in the training mix.
Why this is correct
General data acts as a regularizer to maintain base knowledge.
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