MLS-C01 Practice Question: Machine Learning Implementation and Operations
Which TWO configuration steps are necessary to deploy a custom Docker container for training in Amazon SageMaker? (Choose two.)
⚠ Common exam trap
Test-takers frequently confuse the requirements for a training container versus an inference container, thinking that exposing an API endpoint or pushing to Docker Hub is necessary for training, when SageMaker strictly enforces the `/opt/ml` directory contract and uses Amazon ECR for image storage.
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
✓
Implement the train function in the container that saves model artifacts to /opt/ml/model
Amazon SageMaker expects the training container to save model artifacts to the `/opt/ml/model` directory, which SageMaker automatically copies to Amazon S3 after training completes. This is a required contract for any custom training container used with SageMaker.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Expose a REST API endpoint for inference
Why it's wrong here
Training containers do not need to expose endpoints.
- ✓
Implement the train function in the container that saves model artifacts to /opt/ml/model
Why this is correct
SageMaker expects the model to be saved in /opt/ml/model.
- ✗
Define a Docker Compose file to manage multi-container training
Why it's wrong here
SageMaker does not use Docker Compose.
- ✓
Include a training script that reads hyperparameters from /opt/ml/input/config/hyperparameters.json
Why this is correct
SageMaker passes hyperparameters in this file.
- ✗
Push the container image to Docker Hub
Why it's wrong here
SageMaker requires the image to be in Amazon ECR.
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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