AI0-001 Implementing AI Solutions Practice Question
A data scientist is fine-tuning a large language model for a domain-specific task using QLoRA. Which TWO statements correctly describe QLoRA's advantages?
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
✓
It enables fine-tuning on consumer-grade GPUs by reducing memory requirements
QLoRA uses 4-bit quantization to reduce memory, and freezes the base model while training low-rank adapters. It does not reduce inference latency (base model still runs), and does not require more data than full 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.
- ✓
It enables fine-tuning on consumer-grade GPUs by reducing memory requirements
Why this is correct
Lower memory allows fine-tuning on smaller GPUs, such as consumer-grade hardware.
- ✓
It reduces memory usage by quantizing the base model to 4-bit precision
Why this is correct
QLoRA quantizes the base model to 4-bit, significantly lowering memory footprint.
- ✗
It requires more training data than full fine-tuning to achieve comparable accuracy
Why it's wrong here
QLoRA typically needs similar or less data; it does not inherently require more.
- ✗
It trains the full model parameters with low precision
Why it's wrong here
QLoRA freezes the base model; only low-rank adapters are trained, not full parameters.
- ✗
It increases inference speed compared to the base model
Why it's wrong here
QLoRA does not change the base model architecture; inference speed is similar or slightly slower due to adapter computations.
About these practice questions
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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.