MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data scientist needs to run a one-time training job on a large dataset using SageMaker. The job requires a specific PyTorch version and custom dependencies. Which approach is MOST efficient?
⚠ Common exam trap
The MLS-C01 exam often tests the distinction between using a fully managed estimator (like PyTorch) versus manual containerization or notebook-based training, where candidates may overcomplicate the solution by choosing custom Docker (Option A) due to familiarity with containerization, missing that pre-built containers are more efficient for standard frameworks.
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 the SageMaker PyTorch estimator with a pre-built container.
The SageMaker PyTorch estimator provides a pre-built, optimized container with the specified PyTorch version, eliminating the need to manage custom Docker images or manual dependency installation. For a one-time training job, this approach is the most efficient as it requires minimal setup and leverages SageMaker's managed infrastructure for training.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create a custom Docker container and push to ECR.
Why it's wrong here
Custom containers are needed for non-standard environments, but PyTorch is already supported.
- ✗
Launch a SageMaker notebook instance, install dependencies, and run training script.
Why it's wrong here
Notebook instances are for development, not for one-time training jobs.
- ✓
Use the SageMaker PyTorch estimator with a pre-built container.
Why this is correct
The framework estimator manages the container and allows adding custom dependencies via source_dir.
- ✗
Use the SageMaker generic container and install PyTorch via a lifecycle configuration.
Why it's wrong here
The generic container requires manual setup and is less efficient.
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