MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company is using Amazon SageMaker to train a model using a custom Docker container. The training script writes model artifacts to the `/opt/ml/model` directory. The training job completes successfully, but the model artifacts are not uploaded to the S3 output path specified in the training job. The company has verified that the SageMaker execution role has the necessary S3 permissions. The Docker container is built using a base image that is not one of the official SageMaker Docker images. What is the MOST likely reason for the failure to upload model artifacts?
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 custom container does not include the SageMaker training toolkit, which handles artifact uploads.
The SageMaker training toolkit is required in custom containers to handle the automatic upload of model artifacts to S3. Without it, even if the script writes to /opt/ml/model and has correct permissions, SageMaker cannot perform the upload. Option B is correct because the container lacks the toolkit. Option A is incorrect because the entry point is unrelated to the upload failure. Option C is incorrect as the output path formatting would cause a different error. Option D is incorrect because permissions were already verified.
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 training script's entry point is not correctly specified in the container.
Why it's wrong here
Wrong: The script runs successfully, so entry point is likely correct.
- ✓
The custom container does not include the SageMaker training toolkit, which handles artifact uploads.
Why this is correct
Correct: Without the toolkit, SageMaker does not automatically upload artifacts.
- ✗
The output path in the training job configuration is incorrectly formatted.
Why it's wrong here
Wrong: The output path is specified correctly and the job completes.
- ✗
The SageMaker execution role does not have s3:PutObject permission on the output bucket.
Why it's wrong here
Wrong: Permissions have been verified.
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
This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.