1Z0-1127-25 Fundamentals of Large Language Models Practice Question
A data scientist is using OCI Data Science to fine-tune a Cohere command model on domain-specific documents. They observe that the fine-tuned model generates repetitive text. What is the most likely cause?
⚠ Common exam trap
The trap here is that candidates often blame hyperparameters (epochs, learning rate, batch size) for overfitting symptoms, but The 1Z0-1127 exam specifically tests the understanding that data diversity is the root cause of repetitive generation in fine-tuned LLMs.
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 training dataset lacked diversity.
Repetitive text in fine-tuned models is a classic symptom of overfitting to a narrow or homogeneous training dataset. When the domain-specific documents lack diversity in phrasing, topics, or contexts, the model learns to latch onto the most common patterns and repeats them, rather than generalizing. This is not a hyperparameter tuning issue but a data quality issue.
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 number of epochs was insufficient.
Why it's wrong here
Insufficient epochs leads to underfitting, not repetitive text.
- ✓
The training dataset lacked diversity.
Why this is correct
Lack of diversity in training data leads to overfitting and repetitive outputs.
- ✗
The learning rate was too high.
Why it's wrong here
High learning rate typically causes training to diverge, not produce repetitive text.
- ✗
The batch size was too small.
Why it's wrong here
Small batch size can introduce noise but does not directly cause repetitive outputs.
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