MLA-C01 ML Model Development Practice Question
A data scientist wants to fine-tune a Llama 2 7B model using SageMaker for a text summarization task. The dataset is 10 GB. The budget is limited, so cost efficiency is important. Which THREE steps should the data scientist take? (Choose THREE.)
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 LoRA to reduce the number of trainable parameters
LoRA reduces trainable parameters, enabling fine-tuning on smaller instances. HuggingFace estimator is the standard for HF models. Spot instances reduce cost. DeepSpeed ZeRO-3 is for large models but not necessary with LoRA. BYOC is overkill.
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 SageMaker Debugger to reduce training time
Why it's wrong here
Debugger monitors training but doesn't directly reduce time or cost.
- ✗
Use the SageMaker built-in BlazingText algorithm
Why it's wrong here
BlazingText is for text classification and word2vec, not for LLM fine-tuning.
- ✓
Use LoRA to reduce the number of trainable parameters
Why this is correct
LoRA enables efficient fine-tuning with much lower memory requirements.
- ✓
Use managed spot training
Why this is correct
Spot instances provide significant cost savings for long-running jobs.
- ✓
Use the SageMaker HuggingFace estimator
Why this is correct
The HuggingFace estimator is designed for fine-tuning HF models and simplifies the process.
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