1Z0-1127-25 Fundamentals of Large Language Models Practice Question
Which TWO are advantages of using LoRA for fine-tuning?
⚠ Common exam trap
Oracle often tests the misconception that reducing trainable parameters automatically improves inference speed, but LoRA's memory and parameter savings apply only to training, not to inference latency.
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
✓
Requires less GPU memory
LoRA (Low-Rank Adaptation) reduces GPU memory requirements because it freezes the original model weights and injects trainable low-rank matrices into specific layers. This means only a tiny fraction of parameters need gradients and optimizer states, drastically lowering memory consumption during fine-tuning compared to full fine-tuning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Requires less GPU memory
Why this is correct
Fewer trainable parameters means lower memory usage during training.
- ✗
Guarantees higher accuracy
Why it's wrong here
LoRA may match full fine-tuning accuracy but does not guarantee improvement.
- ✓
Reduces number of trainable parameters
Why this is correct
LoRA only updates small low-rank matrices, significantly reducing parameters.
- ✗
Increases model size
Why it's wrong here
LoRA adds a small number of parameters, not increases the base model size.
- ✗
Improves inference speed
Why it's wrong here
LoRA does not directly affect inference speed; it is a training technique.
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