- A
Full fine-tuning of all model parameters
Why wrong: Full fine-tuning requires updating all parameters, which is memory-intensive and infeasible with limited GPU memory for large models.
- B
QLoRA (Quantized Low-Rank Adaptation)
QLoRA quantizes the base model to 4-bit and applies low-rank adapters, enabling fine-tuning with minimal memory without sacrificing performance.
- C
Instruction tuning with the full dataset
Why wrong: Instruction tuning can be done with PEFT, but without quantization, memory requirements remain high.
- D
Retrieval-Augmented Generation (RAG) without fine-tuning
Why wrong: RAG does not adapt the model to the specific domain; it only retrieves relevant documents, which may not be sufficient for domain-specific generation.
Question 325 of 1,000
AI0-001 Implementing AI Solutions Practice Question
This AI0-001 practice question tests your understanding of implementing ai solutions. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 requires updating all parameters, which is memory-intensive and infeasible with limited GPU memory for large models.
- ✓
QLoRA (Quantized Low-Rank Adaptation)
Why this is correct
QLoRA quantizes the base model to 4-bit and applies low-rank adapters, enabling fine-tuning with minimal memory without sacrificing performance.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Instruction tuning with the full dataset
Why it's wrong here
Instruction tuning can be done with PEFT, but without quantization, memory requirements remain high.
- ✗
Retrieval-Augmented Generation (RAG) without fine-tuning
Why it's wrong here
RAG does not adapt the model to the specific domain; it only retrieves relevant documents, which may not be sufficient for domain-specific generation.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco 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.
Detailed technical explanation
How to think about this question
QLoRA works by first quantizing the pre-trained model weights to 4-bit NormalFloat (NF4) using a double quantization technique, then adding low-rank adapter matrices (typically rank r=8 or r=16) that are trained in full precision. During backpropagation, gradients flow only through the adapters, while the quantized base model remains frozen, reducing memory from ~16GB (for a 7B model in FP16) to under 6GB. A real-world scenario is fine-tuning Llama 2 7B on a medical Q&A dataset with a single RTX 3090 (24GB), where QLoRA enables training that would otherwise require multiple GPUs.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this AI0-001 question test?
Implementing AI Solutions — This question tests Implementing AI Solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this AI0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jul 4, 2026
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.
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