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?
⚠ Common exam trap
MLA-C01 often tests the distinction between parameter-efficient fine-tuning (LoRA) and memory-optimized fine-tuning (QLoRA), tricking candidates into selecting plain LoRA when the question specifically emphasizes reducing memory footprint.
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 combines 4-bit quantization of the base model weights (via NF4) with LoRA adapters, dramatically reducing GPU memory during fine-tuning while preserving accuracy close to full fine-tuning. The 4-bit quantization is the key memory-saving mechanism, since the frozen base weights consume ~4x less memory than fp16/fp32. LoRA alone reduces optimizer/gradient memory but still loads the base model in higher precision, so QLoRA is the correct answer for minimizing memory footprint.
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 with fp32 keeps base weights and adapters in full precision, so the frozen parameter memory and optimiser states remain large. LoRA is the right family of technique, but fp32 negates the footprint reduction that quantised or bf16 variants deliver; the precision choice is the failing element.
- ✓
Use QLoRA (Quantized Low-Rank Adaptation) with 4-bit quantization
Why this is correct
QLoRA quantises the frozen base model to 4-bit and trains only low-rank adapters, sharply reducing GPU memory versus full or standard LoRA fine-tuning. This directly satisfies the memory-footprint constraint while retaining most task accuracy for the LLM fine-tuning job.
- ✗
Use SageMaker Model Parallelism with tensor parallelism
Why it's wrong here
Tensor parallelism shards individual layer computations across GPUs, which reduces per-device memory but adds substantial inter-GPU communication overhead and is intended for models too large for one device. For a single-node footprint reduction, parameter-efficient fine-tuning achieves the goal without the communication cost.
- ✗
Full fine-tuning on a p3.16xlarge instance
Why it's wrong here
Full fine-tuning updates every weight and stores full optimiser states, so memory scales with total parameters regardless of instance size. A larger p3 instance adds GPU memory capacity but does not change the training method's footprint; full fine-tuning is chosen when maximum task accuracy justifies that cost.
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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
Why D: QLoRA (Quantized LoRA) extends LoRA by quantizing the frozen base model weights to 4-bit precision while keeping the LoRA adapters in higher precision, dramatically reducing GPU memory during fine-tuning. Since the team already uses LoRA and wants to further reduce memory footprint, QLoRA is the correct combination. It is specifically designed as a memory-reduction technique layered on top of LoRA.
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 (Quantized LoRA) extends LoRA by quantizing the frozen base model weights to 4-bit precision (typically NF4) while keeping the LoRA adapters in higher precision during training. This dramatically reduces GPU memory because the base model — which is the largest memory consumer — is stored in 4-bit instead of 16-bit or 32-bit, while still allowing effective fine-tuning of the low-rank adapters. Combining LoRA with QLoRA is the standard technique for reducing memory during fine-tuning of large foundation models.
JA
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Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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