MLS-C01 Practice Question: Machine Learning Implementation and Operations
A machine learning team is using SageMaker to train a model with a custom Docker container. The training script runs locally but fails on SageMaker with a 'Permission denied' error when writing to /opt/ml/model. What is the 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 container's user does not have write permission to /opt/ml/model
In SageMaker, the training container is expected to store the trained model artifacts in the /opt/ml/model directory. If the user running the training script inside the container does not have write permissions to that directory, the training will fail with a 'Permission denied' error. Option A is correct. Option B (image too large) would cause different errors, such as EBS volume limits. Option C refers to input data paths; the error is about writing the model, not reading inputs. Option D (S3 bucket) would cause read errors, not a write permission issue.
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's user does not have write permission to /opt/ml/model
Why this is correct
Correct. The container user lacks write permission to /opt/ml/model, which is required for saving the model artifact.
- ✗
The Docker image is too large
Why it's wrong here
Incorrect. A Docker image that is too large might cause other issues, but not a 'Permission denied' error when writing to /opt/ml/model.
- ✗
The training script is trying to read from /opt/ml/input/data instead of /opt/ml/input/data/training
Why it's wrong here
Incorrect. The error is about writing to /opt/ml/model, not about reading input data paths.
- ✗
The training data is not in the correct S3 bucket
Why it's wrong here
Incorrect. If the training data were not in the correct S3 bucket, the error would be about reading data, not about writing to /opt/ml/model.
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 |
Go deeper
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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