MLS-C01 Modeling Practice Question
Exhibit
Refer to the exhibit. ``` [Container] Training completed. [Container] Uploading model artifacts to S3: s3://my-bucket/model.tar.gz [Container] Training job completed ```
Refer to the exhibit. The log shows the end of a successful SageMaker training job. However, the ML engineer cannot find the model artifacts in the specified S3 bucket. What is the most likely cause?
⚠ Common exam trap
AWS often tests the misconception that a successful training job log implies the model artifacts were successfully uploaded, when in fact the IAM role permissions are the gatekeeper for S3 write operations, and a missing permission can cause silent failures.
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 IAM role used by the training job does not have permission to write to the S3 bucket.
The training job completed successfully, meaning the SageMaker training container executed without errors. However, if the model artifacts are not found in the specified S3 bucket, the most likely cause is that the IAM role associated with the training job lacks the necessary s3:PutObject permission for that bucket. SageMaker uses the role's credentials to write the output; without write access, the artifacts are silently dropped or fail to upload, even though the training code itself may have run to completion.
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 IAM role used by the training job does not have permission to write to the S3 bucket.
Why this is correct
Without s3:PutObject, the upload fails.
- ✗
The S3 bucket does not exist.
Why it's wrong here
The job would have failed before training.
- ✗
The model artifacts were uploaded to a different S3 path.
Why it's wrong here
The log shows the correct path.
- ✗
The training job did not have network access to S3.
Why it's wrong here
Network access is typically configured; lack would cause other errors.
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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