MLS-C01 Modeling Practice Question
Network Topology
A data scientist runs a SageMaker training job and receives the above error. The S3 bucket 'my-bucket' contains a folder 'data' with a file 'data.csv'. What is the MOST likely cause of the error?
⚠ Common exam trap
Many candidates confuse S3 prefixes (folders) with S3 objects (files), assuming SageMaker can automatically resolve a folder to its contents, when in fact it requires an explicit file path for training data channels.
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 S3 URI should be s3://my-bucket/data/data.csv instead of s3://my-bucket/data
The error occurs because the SageMaker training job expects a specific S3 object URI (pointing to a file), not a prefix (pointing to a folder). When you specify `s3://my-bucket/data`, SageMaker interprets it as a prefix and attempts to list objects under that prefix, but the training channel requires a direct file reference. Using `s3://my-bucket/data/data.csv` provides the exact object path, allowing SageMaker to download the file correctly.
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 instance type ml.m5.large does not have enough memory
Why it's wrong here
Memory is not the issue indicated by the error.
- ✗
The VolumeSizeInGB is too small to download the data
Why it's wrong here
Volume size is sufficient for a small file.
- ✓
The S3 URI should be s3://my-bucket/data/data.csv instead of s3://my-bucket/data
Why this is correct
If the training script expects a single file, the S3 URI must point to the file directly.
- ✗
The S3 bucket and the training job are in different regions
Why it's wrong here
Cross-region access would cause a different error.
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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