MLS-C01 Modeling Practice Question
A data scientist is using SageMaker to train a deep learning model with a large dataset stored in S3. The training is taking a long time. Which action would most likely reduce training time without sacrificing accuracy?
⚠ Common exam trap
A common mix-up: candidates confuse batch size adjustments (which affect convergence stability) with I/O optimization techniques, overlooking that SageMaker Pipe mode directly addresses the data loading bottleneck without altering the training algorithm.
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 Input mode
SageMaker Pipe Input mode streams training data directly from S3 into the algorithm without first downloading it to the local EBS volume. This eliminates the I/O bottleneck caused by large dataset downloads, significantly reducing training time while preserving accuracy because the model sees the same data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the batch size
Why it's wrong here
May reduce accuracy.
- ✓
Use SageMaker Pipe Input mode
Why this is correct
Streams data from S3 directly to the algorithm, reducing I/O time.
- ✗
Use a smaller instance type
Why it's wrong here
Would increase training time.
- ✗
Reduce the number of epochs
Why it's wrong here
Would sacrifice accuracy.
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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