MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data scientist is using Amazon SageMaker to train a model using a built-in algorithm. The training job uses a large dataset stored in Amazon S3, and the scientist wants to use pipe mode to stream the data directly from S3 to the training instance, reducing the time needed to download the data. The training job is configured with 'InputMode' set to 'Pipe'. However, the training job fails with an error indicating that the algorithm does not support pipe mode. What should the scientist do to resolve this issue?
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
✓
Change the 'InputMode' to 'File'
When a built-in algorithm does not support pipe mode, the simplest solution is to change the InputMode to 'File', which downloads the entire dataset before training. Option B is incorrect because pipe mode support depends on the algorithm, not the instance type. Option C is incorrect because AWS Glue is used for ETL and cannot directly stream data to a SageMaker training job. Option D is incorrect because while switching to an algorithm that supports pipe mode is possible, it may be unnecessary if the current algorithm works well with file mode, and changing the input mode is a simpler fix without altering the algorithm.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Change the 'InputMode' to 'File'
Why this is correct
Changing InputMode to 'File' resolves the issue because the algorithm works with file mode, which downloads the data fully before training. This is the simplest fix.
- ✗
Use a different instance type that supports pipe mode
Why it's wrong here
The instance type does not affect pipe mode support; pipe mode is a feature of the algorithm and the SageMaker framework, not the instance.
- ✗
Use AWS Glue to stream the data to the training instance
Why it's wrong here
AWS Glue is a data integration service for ETL jobs, not designed to stream data directly to SageMaker training instances.
- ✗
Switch to a different built-in algorithm that supports pipe mode
Why it's wrong here
Switching to a different algorithm that supports pipe mode is a valid option, but it is more invasive than simply changing the input mode, and the current algorithm may be preferred for its performance or accuracy.
Visual reference
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 |
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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.