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AI0-001 Implementing AI Solutions Practice Question

A data science team is fine-tuning a large language model for a domain-specific task using LoRA. They have a limited GPU budget and want to minimize memory usage during training. Which technique should they use?

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 (Quantized LoRA) with a 4-bit quantized base model

QLoRA (Quantized LoRA) quantizes the base model to 4-bit, drastically reducing memory usage while still applying LoRA adapters. Standard LoRA uses full precision. PEFT is a category, not a specific technique. Full fine-tuning uses the most memory.

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) with the base model in full precision

    Why it's wrong here

    LoRA reduces trainable parameters but the base model still consumes memory in full precision (e.g., 16-bit), which may still be high.

  • Use PEFT (Parameter-Efficient Fine-Tuning) without specifying a specific method

    Why it's wrong here

    PEFT is a family of methods; not specifying a technique does not guarantee minimal memory usage. QLoRA is the specific method that minimizes memory.

  • Use QLoRA (Quantized LoRA) with a 4-bit quantized base model

    Why this is correct

    QLoRA quantizes the base model to 4-bit, significantly reducing memory usage while applying LoRA adapters for efficient fine-tuning.

  • Full fine-tuning of the entire model

    Why it's wrong here

    Full fine-tuning requires updating all parameters and uses the most memory, not suitable for limited GPU budget.

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Written by Johnson Ajibi, MSc IT Security

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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.