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?
⚠ Common exam trap
The trap is confusing LoRA with QLoRA — candidates may think LoRA alone already minimizes memory, but LoRA still stores the base model in 16-bit; only QLoRA's 4-bit quantization of the base model provides the additional memory reduction.
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 extends LoRA by quantizing the frozen base model to 4-bit precision (using NF4 quantization) while keeping LoRA adapters in higher precision, dramatically reducing GPU memory for the base weights. This allows fine-tuning of large models on a single consumer GPU with quality comparable to 16-bit LoRA. The 4-bit base model is dequantized on the fly during forward and backward passes, so memory savings come primarily from storing the base weights in 4 bits instead of 16.
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
Fine-tuning all layers trains every weight, so optimiser states and gradients consume memory proportional to the full parameter count — the opposite of the reduction required. Full fine-tuning suits scenarios where ample GPU memory exists and maximum task-specific accuracy is needed, but it cannot deliver LoRA's memory savings.
- ✗
Increase the rank of LoRA adapters
Why it's wrong here
Increasing the LoRA rank enlarges the trainable adapter matrices, raising the count of trainable parameters, gradients and optimiser states held in memory. It would suit a scenario where quality is lacking and GPU memory is plentiful, not this constrained one.
- ✓
Use QLoRA with 4-bit quantization of the base model
Why this is correct
QLoRA quantises the frozen base weights to 4-bit NF4 while training LoRA adapters in higher precision, cutting GPU memory far below standard LoRA. This satisfies the limited-memory constraint, and because only adapters are trained, fine-tuning quality stays comparable.
- ✗
Use a larger batch size
Why it's wrong here
A larger batch size increases activation memory, worsening the GPU constraint rather than reducing it. Larger batches suit throughput-oriented training on ample hardware, where gradient noise falls and hardware utilisation rises. Here the stem demands lower memory, which gradient checkpointing or quantised LoRA adapters deliver.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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.