1Z0-1127-25 Fundamentals of Large Language Models Practice Question
Network Topology
Refer to the exhibit. What is the solution?
⚠ Common exam trap
Oracle often tests the distinction between hyperparameter tuning (learning rate, epochs) and fundamental model capability (fine-tuning support), leading candidates to mistakenly choose a hyperparameter adjustment when the core issue is that the model cannot be fine-tuned at all.
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
✓
Use a different base model that supports fine-tuning.
The exhibit indicates that the base model does not support fine-tuning, which is a prerequisite for adapting a large language model to a specific task or domain. Using a different base model that supports fine-tuning allows the model to be customized through supervised learning on task-specific data, enabling it to learn new patterns and improve performance. This is the correct solution because without fine-tuning capability, the model cannot be effectively adapted regardless of other hyperparameter adjustments.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use a different base model that supports fine-tuning.
Why this is correct
The error indicates the base model does not support fine-tuning; switch to a supported model.
- ✗
Change the learning rate.
Why it's wrong here
Learning rate is fine-tuning parameter but does not enable fine-tuning on an unsupported model.
- ✗
Increase the training epochs.
Why it's wrong here
Epochs do not affect model support for fine-tuning.
- ✗
Use a different compartment.
Why it's wrong here
Compartment does not affect model capabilities.
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