MLS-C01 Modeling Practice Question
A company uses Amazon SageMaker to train a model. The training job runs successfully but the model artifacts are not saved to the specified S3 output path. What is a likely cause?
⚠ Common exam trap
Candidates often assume any successful training job automatically saves artifacts, but SageMaker only uploads what is explicitly placed in `/opt/ml/model`, and the exam tests this specific SageMaker convention.
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 training script does not save the model to /opt/ml/model.
Amazon SageMaker expects the training script to save the model artifacts to the `/opt/ml/model` directory. After the training job completes, SageMaker automatically copies the contents of this directory to the specified S3 output path. If the script saves the model elsewhere (e.g., `/tmp` or a custom path), no artifacts will be uploaded, resulting in an empty or missing S3 output.
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 does not save the model to /opt/ml/model.
Why this is correct
SageMaker uploads contents of /opt/ml/model to S3; saving elsewhere means artifacts are lost.
- ✗
The model size exceeds the S3 bucket limit.
Why it's wrong here
S3 has no size limit; there is a per-object limit of 5TB.
- ✗
The training job used spot instances.
Why it's wrong here
Spot instances may be interrupted, but if job completes, artifacts are saved.
- ✗
The S3 bucket is in a different AWS Region.
Why it's wrong here
SageMaker can access buckets in other regions with proper permissions.
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.