1Z0-1127-25 Fundamentals of Large Language Models Practice Question
An AI specialist is troubleshooting why a fine-tuned model produces inconsistent results across different inference calls. What is the most likely cause?
⚠ Common exam trap
Oracle often tests the misconception that overfitting (Option C) causes inconsistency, but overfitting actually reduces variance by memorizing patterns; the trap is confusing output variability with poor generalization.
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 temperature is set too high
Temperature controls the randomness of token sampling during inference. A high temperature (e.g., >1.0) increases the probability of selecting less likely tokens, causing the model to produce varied outputs for the same input across different calls. This is the most direct cause of inconsistent results when the base model and fine-tuning are otherwise sound.
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 base model is not suitable
Why it's wrong here
Unsuitable base model leads to consistently poor quality, not inconsistency.
- ✓
The temperature is set too high
Why this is correct
High temperature increases randomness, causing variable outputs.
- ✗
The model is overfitted
Why it's wrong here
Overfitting leads to consistent but poor generalization, not inconsistency.
- ✗
The fine-tuning dataset is too small
Why it's wrong here
Small dataset may cause overfitting, not necessarily inconsistency.
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