1Z0-1127-25 Fundamentals of Large Language Models Practice Question
A data scientist is using OCI Data Science with the Generative AI service to fine-tune a Cohere Command model on a custom dataset of customer support tickets. After training, the model produces poor, irrelevant responses. What is the most likely cause?
⚠ Common exam trap
Oracle often tests the misconception that overfitting (Option C) is the primary cause of poor model output after fine-tuning, but in this scenario the irrelevance points to data insufficiency rather than memorization of training examples.
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
✓
Insufficient training data quality or quantity
Insufficient training data quality or quantity is the most likely cause because fine-tuning a Cohere Command model on a custom dataset of customer support tickets requires a sufficiently large and representative dataset to teach the model domain-specific patterns. If the dataset is too small, noisy, or lacks diversity, the model will fail to generalize and produce irrelevant responses, even with correct tokenization and training hyperparameters.
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 configuration
Why it's wrong here
The tokenizer is automatically selected by the model and cannot be changed.
- ✓
Insufficient training data quality or quantity
Why this is correct
Cohere models need clean, diverse, and task-relevant data; poor data leads to poor fine-tuning.
- ✗
Too many epochs causing overfitting
Why it's wrong here
Overfitting would still produce plausible responses for seen data, not irrelevant ones.
- ✗
Model architecture mismatch between fine-tuned and base model
Why it's wrong here
The fine-tuning process uses the same architecture as the base model.
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