AI0-001 Implementing AI Solutions Practice Question
An organization wants to fine-tune a 7B parameter LLM for a specialized legal document summarization task. They have a small labeled dataset (500 examples) and limited GPU budget. Which THREE techniques should they consider? (Choose three.)
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 LoRA (Low-Rank Adaptation)
PEFT methods like LoRA and QLoRA are designed for efficient fine-tuning with limited resources. Instruction tuning datasets improve task performance. Full fine-tuning is too expensive.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use LoRA (Low-Rank Adaptation)
Why this is correct
LoRA freezes base weights and trains small adapters, drastically reducing memory.
- ✓
Create an instruction-tuning dataset with input-summary pairs
Why this is correct
Instruction tuning adapts the model to follow the summarization task effectively.
- ✗
Train a new model from scratch on legal text
Why it's wrong here
Training a new 7B model from scratch on legal text would require several orders of magnitude more data and compute than the 500-example dataset and limited GPU budget allow, as it demands pre-training on billions of tokens to learn general language structure before any task-specific fine-tuning. This option is tempting because building a domain-specific model from scratch can yield a system deeply specialised to legal terminology, and it would be the correct choice if the organisation had a massive legal corpus (e.g., millions of documents) and extensive GPU resources for full pre-training.
- ✗
Full fine-tuning of all model parameters
Why it's wrong here
Full fine-tuning requires significant memory and compute, not feasible with limited GPU budget.
- ✓
Use QLoRA with 4-bit quantization
Why this is correct
QLoRA further reduces memory by quantizing the base model to 4 bits while using LoRA.
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JA
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.