MLA-C01 ML Model Development Practice Question
A team is fine-tuning a large language model (LLM) using SageMaker and wants to reduce memory footprint 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 Low-Rank Adaptation) with 4-bit quantization
QLoRA (Quantized Low-Rank Adaptation) combines 4-bit quantization with low-rank adapters, significantly reducing GPU memory usage while maintaining model quality.
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 fp32 precision
Why it's wrong here
LoRA reduces trainable parameters but fp32 still consumes significant memory; QLoRA goes further.
- ✓
Use QLoRA (Quantized Low-Rank Adaptation) with 4-bit quantization
Why this is correct
QLoRA uses 4-bit quantization to drastically lower memory usage while preserving performance.
- ✗
Use SageMaker Model Parallelism with tensor parallelism
Why it's wrong here
Model parallelism distributes the model across GPUs but doesn't reduce per-GPU memory as effectively as quantization.
- ✗
Full fine-tuning on a p3.16xlarge instance
Why it's wrong here
Full fine-tuning requires the most memory; p3 instances are older and less memory-efficient.
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Same concept, more angles
2 more ways this is tested on MLA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A team is fine-tuning a foundation model using LoRA for a text summarization task. They want to reduce memory footprint during training. Which technique should they combine with LoRA?
hard- A.Data parallelism
- B.Gradient checkpointing
- C.Mixed precision
- ✓ D.QLoRA
Variation 2. A team is fine-tuning a foundation model using LoRA. They want to reduce memory usage during training. Which technique should they combine LoRA with to further reduce memory?
medium- A.Instruction tuning
- B.Pruning
- C.RLHF
- ✓ D.QLoRA
Why D: QLoRA combines LoRA with quantization (e.g., 4-bit) to drastically reduce memory. Instruction tuning is a method, not a memory reduction technique. RLHF is a training process. Pruning reduces model size but is not typically combined with LoRA in this context.
JA
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