MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data scientist is training a deep learning model on SageMaker using a custom Docker container. The training job fails with an error indicating that the container exited with a non-zero status. The CloudWatch logs show 'FileNotFoundError: [Errno 2] No such file or directory: '/opt/ml/input/data/training/data.csv''. What is the most 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 S3 data source path is incorrect or the data has not been uploaded.
The error indicates that the training data file 'data.csv' is missing at the expected container path '/opt/ml/input/data/training/'. This typically occurs when the S3 data source path specified in the training job configuration is incorrect or the data has not been uploaded to that S3 location. Option A is wrong because the error is about missing data, not the container entry point. Option C is wrong because the error does not relate to training script syntax. Option D is wrong because the error does not mention model artifacts.
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 Docker entry point is misconfigured.
Why it's wrong here
Entry point issues cause different errors.
- ✓
The S3 data source path is incorrect or the data has not been uploaded.
Why this is correct
Missing training data leads to FileNotFoundError.
- ✗
The training script has a syntax error.
Why it's wrong here
Syntax errors would appear earlier.
- ✗
The model output path in the training job configuration is wrong.
Why it's wrong here
Model output path issue would cause failure at saving, not reading input.
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.