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MLA-C01 Practice Question: Deploy a PyTorch model on SageMaker for real-time…
A company wants to deploy a PyTorch model on SageMaker for real-time inference. Which two steps are required? (Select TWO.)
⚠ Common exam trap
Candidates often confuse the optional Model Registry step (B) as mandatory for deployment, or mistakenly think uploading training data (A) is needed for inference, when in fact only the model artifact packaging (C) and endpoint configuration (D) are the two required steps for real-time inference on SageMaker.
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
✓
Package the model artifacts into a tar.gz file.
SageMaker requires model artifacts to be packaged as a single tar.gz file (containing the model weights, serialized PyTorch model, and any dependencies) for deployment. This compressed archive is uploaded to S3 and referenced when creating the model object for real-time inference.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Upload the training data to an S3 bucket.
Why it's wrong here
Training data upload is part of training, not deployment.
- ✗
Register the model in the SageMaker Model Registry.
Why it's wrong here
Model Registry is optional for versioning, not required for deployment.
- ✓
Package the model artifacts into a tar.gz file.
Why this is correct
SageMaker expects model artifacts in a tar.gz format.
- ✓
Create a SageMaker endpoint configuration with the desired instance type.
Why this is correct
Endpoint configuration specifies instance type and other settings.
- ✗
Set up a SageMaker Notebook instance.
Why it's wrong here
Notebook instances are for development and experimentation.
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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