MLS-C01 Practice Question: Machine Learning Implementation and Operations
A machine learning engineer is deploying a model on Amazon SageMaker that was trained using a custom Docker container. The container is stored in Amazon ECR. The engineer creates a SageMaker model and endpoint configuration, but when creating the endpoint, it fails with an error: 'Could not find the inference code at the expected path.' The engineer verified that the container image is correct and the model artifacts are in S3. What is the most likely cause?
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
✓
The inference code is not placed in the /opt/ml/model directory inside the container.
The error 'Could not find the inference code at the expected path' indicates that SageMaker cannot locate the inference script (e.g., serve.py, inference.py) inside the container. SageMaker expects the inference code to be placed in the /opt/ml/model/ directory within the container. Option D correctly identifies that the inference code is not placed in /opt/ml/model. Option A is incorrect because container compatibility would cause a different error (e.g., unsupported base image). Option B is incorrect because ECR pull permissions typically result in an 'Unauthorized' error when pulling the image. Option C is incorrect because model artifacts being in the wrong format would cause loading errors, not inference code path errors.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The container is not compatible with the SageMaker inference environment.
Why it's wrong here
Compatibility issues would cause runtime errors, not missing code.
- ✗
The SageMaker execution role does not have ECR pull permissions.
Why it's wrong here
That would cause an 'access denied' error, not missing code.
- ✗
The model artifacts are not in the correct format.
Why it's wrong here
Artifact format does not affect inference code path.
- ✓
The inference code is not placed in the /opt/ml/model directory inside the container.
Why this is correct
SageMaker expects code in /opt/ml/model for custom containers.
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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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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