AI0-001 Implementing AI Solutions Practice Question
An AI practitioner is fine-tuning a large language model for a domain-specific task using a small labeled dataset (500 examples). They have limited GPU memory. Which technique is MOST suitable?
⚠ Common exam trap
The AI0-001 exam often tests the misconception that 'fine-tuning always means updating all parameters' or that 'RAG alone can replace fine-tuning for domain adaptation,' leading candidates to overlook memory-efficient adapter methods like QLoRA.
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 (Quantized Low-Rank Adaptation)
QLoRA (Quantized Low-Rank Adaptation) is the most suitable technique because it combines 4-bit quantization of the base model with low-rank adapter modules, drastically reducing GPU memory usage while still allowing fine-tuning on a small dataset. This approach preserves the model's pre-trained knowledge and avoids catastrophic forgetting, which is critical when only 500 labeled examples are available.
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 of all model parameters
Why it's wrong here
Full fine-tuning updates every weight, so gradients and optimiser states for a large model far exceed limited GPU memory. It suits abundant data and ample hardware. With 500 examples, parameter-efficient methods such as LoRA train small adapters instead, avoiding that memory cost.
- ✓
QLoRA (Quantized Low-Rank Adaptation)
Why this is correct
QLoRA quantises the frozen base weights to 4-bit and trains small low-rank adapters, slashing GPU memory far below full fine-tuning. With only 500 labelled examples and constrained VRAM, this parameter-efficient method fits the domain task without exhausting memory.
- ✗
Instruction tuning with the full dataset
Why it's wrong here
Instruction tuning still updates the full parameter set, so optimiser and gradient memory exceed the limited GPU. It suits large, diverse instruction datasets teaching general task-following. With only 500 examples, parameter-efficient fine-tuning such as LoRA adapts the model within memory.
- ✗
Retrieval-Augmented Generation (RAG) without fine-tuning
Why it's wrong here
RAG retrieves external context at inference but leaves model weights unchanged, so it cannot adapt behaviour to the labelled examples the task requires. It suits knowledge-intensive question answering over a document corpus, not domain-specific fine-tuning of a small labelled set under memory limits.
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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.