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Fundamentals of Large Language ModelseasyMultiple SelectObjective-mapped

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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