MLS-C01 Practice Question: Machine Learning Implementation and Operations
A team is training an XGBoost model using SageMaker with a large dataset in S3 (100 GB). Training is taking too long. Which change will most likely reduce training time without sacrificing accuracy?
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
✓
Configure Pipe mode for data input
Configuring Pipe mode for data input streams data directly from S3 to the training algorithm, significantly reducing I/O overhead and training time without affecting model accuracy. Option A (reducing the number of training instances) would actually increase training time, not reduce it. Option C (enabling SageMaker Managed Spot Training) is primarily for cost savings and does not reduce training time. Option D (using a larger instance type) may provide some speedup but is less effective than addressing the I/O bottleneck with Pipe mode, and it may increase costs unnecessarily.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the number of training instances
Why it's wrong here
Fewer instances would increase training time.
- ✓
Configure Pipe mode for data input
Why this is correct
Pipe mode streams data directly from S3, reducing I/O bottleneck and training time.
- ✗
Enable SageMaker Managed Spot Training
Why it's wrong here
Spot Training reduces cost but does not reduce training time.
- ✗
Use a larger instance type with more vCPUs
Why it's wrong here
Larger instances can help but may not be the most effective; distributed training often scales better.
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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