AI0-001 Implementing AI Solutions Practice Question
An AI engineer is selecting a PEFT technique to fine-tune a large language model. Which TWO are examples of PEFT (Parameter-Efficient Fine-Tuning)?
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
✓
LoRA
LoRA and QLoRA are popular PEFT methods that update only a small number of additional parameters while keeping the base model frozen.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Instruction tuning on a large dataset
Why it's wrong here
Instruction tuning is a training approach, not a specific PEFT technique.
- ✓
LoRA
Why this is correct
LoRA adds low-rank matrices to transformer layers, a standard PEFT method.
- ✓
QLoRA
Why this is correct
QLoRA extends LoRA with quantization, another PEFT technique.
- ✗
Gradient checkpointing
Why it's wrong here
Gradient checkpointing reduces memory usage but does not reduce the number of trainable parameters.
- ✗
Full fine-tuning of all parameters
Why it's wrong here
Full fine-tuning updates all parameters, not parameter-efficient.
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Written by Johnson Ajibi, MSc IT Security
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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.