AI0-001 Implementing AI Solutions Practice Question
A team is fine-tuning a large language model using LoRA. They have limited GPU memory. Which technique can further reduce memory consumption while maintaining similar fine-tuning quality?
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 QLoRA with 4-bit quantization of the base model
QLoRA combines 4-bit quantization of the base model with LoRA adapters, drastically reducing memory usage while preserving fine-tuning performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Fine-tune all layers instead of using LoRA
Why it's wrong here
Full fine-tuning uses much more memory than LoRA, counterproductive to memory reduction.
- ✗
Increase the rank of LoRA adapters
Why it's wrong here
Increasing rank adds more trainable parameters, which increases memory consumption, not reduces it.
- ✓
Use QLoRA with 4-bit quantization of the base model
Why this is correct
QLoRA quantizes the base model to 4 bits, significantly reducing memory while LoRA adapters handle fine-tuning.
- ✗
Use a larger batch size
Why it's wrong here
Larger batch sizes increase memory usage, the opposite of the goal.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI0-001 exam.