- A
Use QLoRA with 4-bit quantized base model
QLoRA quantizes the base model to 4-bit, reducing memory while keeping LoRA adapters for fine-tuning.
- B
Use a larger batch size to speed up training
Why wrong: Larger batch size increases memory usage.
- C
Fine-tune all layers of the base model
Why wrong: Full fine-tuning uses more memory than LoRA.
- D
Increase the rank of LoRA adapters
Why wrong: Higher rank increases memory usage.
Question 249 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.
A company is fine-tuning an LLM for a domain-specific task using LoRA. They have limited GPU memory and need to reduce memory footprint without sacrificing fine-tuning quality. Which approach should they consider?
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 QLoRA with 4-bit quantized base model
QLoRA combines 4-bit NormalFloat quantization of the base model with LoRA adapters, drastically reducing GPU memory usage while preserving fine-tuning quality through techniques like double quantization and paged optimizers. This directly addresses the constraint of limited GPU memory without sacrificing the model's ability to learn domain-specific tasks effectively.
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.
- ✓
Use QLoRA with 4-bit quantized base model
Why this is correct
QLoRA quantizes the base model to 4-bit, reducing memory while keeping LoRA adapters for fine-tuning.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a larger batch size to speed up training
Why it's wrong here
Larger batch size increases memory usage.
- ✗
Fine-tune all layers of the base model
Why it's wrong here
Full fine-tuning uses more memory than LoRA.
- ✗
Increase the rank of LoRA adapters
Why it's wrong here
Higher rank increases memory usage.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that increasing model capacity (e.g., higher LoRA rank or full fine-tuning) always improves quality, when in fact memory-constrained environments require efficient techniques like QLoRA that balance resource usage and performance.
Detailed technical explanation
How to think about this question
QLoRA uses 4-bit NormalFloat quantization, which is information-theoretically optimal for normally distributed weights, and applies double quantization to also quantize the quantization constants, saving additional memory. A real-world scenario is fine-tuning a 65B parameter model on a single 48GB GPU, where QLoRA enables this task that would otherwise require multiple high-memory GPUs, while maintaining performance within 1% of full fine-tuning.
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.
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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: Use QLoRA with 4-bit quantized base model — QLoRA combines 4-bit NormalFloat quantization of the base model with LoRA adapters, drastically reducing GPU memory usage while preserving fine-tuning quality through techniques like double quantization and paged optimizers. This directly addresses the constraint of limited GPU memory without sacrificing the model's ability to learn domain-specific tasks effectively.
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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