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)?
⚠ Common exam trap
The trap is confusing memory-saving techniques (gradient checkpointing) or training objectives (instruction tuning) with PEFT — candidates must recognize that PEFT specifically means training a small subset of parameters, not just using less memory.
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 (Low-Rank Adaptation) is correct because it freezes the pretrained model weights and injects trainable low-rank decomposition matrices into the transformer layers, updating only a tiny fraction of parameters. QLoRA is correct because it extends LoRA by quantizing the base model to 4-bit (NF4) and adding trainable low-rank adapters, further reducing memory while remaining a parameter-efficient method. Instruction tuning (A) is a training objective/paradigm that typically updates all or many parameters, not a PEFT technique itself. Gradient checkpointing (D) is a memory-saving trick that recomputes activations during backpropagation and does not reduce the number of trainable parameters. Full fine-tuning (E) updates every model parameter, which is the opposite of parameter-efficient 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.
- ✗
Instruction tuning on a large dataset
Why it's wrong here
Instruction tuning updates all or most model weights on a task dataset, so it is full fine-tuning rather than parameter-efficient. It is tempting because it adapts a model to follow prompts, but PEFT methods such as LoRA or prefix tuning freeze the base weights and train a small subset.
- ✓
LoRA
Why this is correct
LoRA freezes the pretrained weights and injects trainable low-rank decomposition matrices into each transformer layer, updating only a tiny fraction of parameters. This satisfies the PEFT constraint of drastically reducing trainable parameters and memory during fine-tuning, unlike full fine-tuning which updates every weight.
- ✓
QLoRA
Why this is correct
QLoRA combines 4-bit quantisation of the frozen base weights with LoRA adapters, so only the low-rank matrices receive gradient updates. This satisfies the stem's parameter-efficiency constraint: trainable parameters stay a tiny fraction of the full model, while memory drops enough to fine-tune large LLMs on modest GPUs.
- ✗
Gradient checkpointing
Why it's wrong here
Gradient checkpointing reduces activation memory during training by recomputing activations in the backward pass; it changes no parameter count and trains all weights. It is tempting because it lowers memory cost, but it is a memory optimisation, not a fine-tuning method like LoRA or adapters.
- ✗
Full fine-tuning of all parameters
Why it's wrong here
Full fine-tuning updates every weight in the model, so it is not parameter-efficient by definition and defeats the purpose of PEFT. It is tempting because it delivers maximum task adaptation and remains the baseline for accuracy, and would be chosen when compute and storage budgets are unconstrained.
About these practice questions
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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
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