MLS-C01 Modeling Practice Question
A data scientist is using Amazon SageMaker to train a model with a large dataset that does not fit into memory on a single instance. The training algorithm supports distributed training. Which approach should the scientist use to train the model efficiently?
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
✓
Use SageMaker Pipe mode to stream data directly from S3
SageMaker Pipe mode streams data from S3 directly to the training algorithm without writing to disk, enabling processing of large datasets beyond memory.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use SageMaker File mode and increase the instance volume size
Why it's wrong here
File mode downloads data to disk, which may not solve memory issue and adds I/O time.
- ✗
Use Amazon EMR to preprocess data and then train on a smaller sample
Why it's wrong here
Downsampling loses data; not efficient for large-scale training.
- ✗
Split the data into smaller files and use multiple training jobs sequentially
Why it's wrong here
Sequential training loses the benefit of distributed learning and may not fit memory.
- ✓
Use SageMaker Pipe mode to stream data directly from S3
Why this is correct
Pipe mode allows the algorithm to read data on the fly, handling large datasets.
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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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.