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.)
⚠ Common exam trap
MLA-C01 often tests whether candidates can distinguish cost-optimization techniques for LLM fine-tuning (LoRA, spot training, HuggingFace estimator) from unrelated tools (Debugger) or algorithms that cannot handle LLMs (BlazingText), so the trap is picking Debugger thinking it speeds up training.
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
Option C is correct because LoRA (Low-Rank Adaptation) freezes the base Llama 2 7B weights and injects small trainable low-rank matrices into the attention layers, cutting the number of trainable parameters and GPU memory/compute needed for fine-tuning, which directly supports the limited budget. Option D is correct because SageMaker managed spot training uses spare EC2 capacity at up to a 90% discount and supports checkpointing to S3 so training resumes after interruptions, making it a key cost-efficiency measure. Option E is correct because the SageMaker HuggingFace estimator provides a prebuilt PyTorch/TensorFlow container with the transformers, datasets, and peft libraries needed to fine-tune Llama 2 7B for summarization without building a custom container. Option A is not correct because SageMaker Debugger is a monitoring and profiling tool for detecting training issues such as vanishing gradients or resource bottlenecks; it does not itself reduce training time. Option B is not correct because BlazingText is a built-in algorithm for text classification and word2vec embeddings, not for fine-tuning large generative LLMs like Llama 2.
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 jobs for anomalies such as vanishing gradients; it does not reduce training time or cost. It is tempting because it improves training efficiency in the sense of debugging, but the scenario needs cost reduction through spot instances, smaller instance types, or parameter-efficient fine-tuning.
- ✗
Use the SageMaker built-in BlazingText algorithm
Why it's wrong here
BlazingText is a built-in algorithm for text classification and word embeddings, not a framework for fine-tuning Llama 2 7B. It is tempting because it is a managed, cost-efficient SageMaker text option, but it cannot load Hugging Face transformer weights or perform summarisation fine-tuning.
- ✓
Use LoRA to reduce the number of trainable parameters
Why this is correct
LoRA freezes the base Llama 2 weights and injects small trainable low-rank matrices into attention layers, cutting trainable parameters and optimiser memory dramatically. This directly addresses the limited budget by reducing GPU hours and memory needed for fine-tuning the 7B model.
- ✓
Use managed spot training
Why this is correct
Managed spot training runs SageMaker jobs on discounted spare EC2 capacity, cutting compute cost by up to 90 percent. SageMaker checkpoints the job and resumes after interruptions, satisfying the cost-efficiency constraint for the fine-tuning workload.
- ✓
Use the SageMaker HuggingFace estimator
Why this is correct
The SageMaker HuggingFace estimator provides a prebuilt container with the transformers library, so the Llama 2 7B model and summarisation script run without building custom training containers. This reduces engineering effort and cost, supporting the budget constraint.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
Go deeper
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Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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