AI0-001 Implementing AI Solutions Practice Question
A company is fine-tuning a large language model using PEFT (Parameter-Efficient Fine-Tuning) to reduce GPU memory usage. They have limited hardware and need to fine-tune a 70B parameter model on a single GPU with 24 GB VRAM. Which technique is MOST suitable?
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
✓
QLoRA (Quantization-aware LoRA) with 4-bit quantization
QLoRA combines quantization (4-bit) and LoRA to fine-tune very large models on limited hardware, achieving significant memory reduction while maintaining 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.
- ✗
Full fine-tuning with gradient checkpointing
Why it's wrong here
Full fine-tuning of a 70B model on a single 24GB GPU is impossible even with gradient checkpointing.
- ✓
QLoRA (Quantization-aware LoRA) with 4-bit quantization
Why this is correct
QLoRA quantizes the base model to 4-bit, drastically reducing memory usage, and uses LoRA adapters for fine-tuning, fitting a 70B model in 24GB VRAM.
- ✗
Instruction tuning with a smaller 7B model
Why it's wrong here
While smaller model is feasible, it does not meet the requirement to fine-tune a 70B model.
- ✗
LoRA (Low-Rank Adaptation) alone
Why it's wrong here
LoRA reduces trainable parameters but the base model still requires full precision memory, likely exceeding 24GB for a 70B model.
About these practice questions
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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.