MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company is deploying a PyTorch model on a SageMaker endpoint for real-time inference. The model is stored as a .pth file in an S3 bucket. The data scientist wants to use the SageMaker PyTorch inference toolkit. Which file is REQUIRED in the model artifacts to serve the model?
⚠ Common exam trap
Many candidates assume a custom inference script (inference.py) is always required, but the SageMaker PyTorch inference toolkit provides a default handler that works with a simple model.pth file, making inference.py optional for basic use cases.
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
✓
A file named model.pth containing the model state dictionary.
The SageMaker PyTorch inference toolkit expects the model artifact to be a single file named model.pth containing the model's state dictionary. When using the default inference handler, the toolkit automatically loads this file into the PyTorch model for serving. No additional inference script is required if the default behavior is sufficient.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A file named model.tar.gz that contains the model and any dependencies.
Why it's wrong here
The artifact must be a tar.gz; inside it, model.pth is the expected file.
- ✗
A file named inference.py that defines the model loading and prediction logic.
Why it's wrong here
inference.py is optional; the toolkit provides default inference if model.pth is present.
- ✓
A file named model.pth containing the model state dictionary.
Why this is correct
The PyTorch inference toolkit loads model.pth by default.
- ✗
A file named requirements.txt listing the dependencies.
Why it's wrong here
requirements.txt is optional; the environment may already have PyTorch.
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 |
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