MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company is using Amazon SageMaker to train a XGBoost model on a large dataset. The training job is taking a long time. The data scientist wants to reduce training time without sacrificing model accuracy. The dataset is 100 GB in CSV format stored in S3. What is the most effective approach?
⚠ Common exam trap
Watch out — candidates often confuse cost optimization (Spot Training) with performance optimization, or incorrectly assume that reducing instances or data size is the only way to speed up training, ignoring SageMaker's specialized data streaming capability.
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 Pipe mode to stream data from S3 instead of downloading it first.
SageMaker's Pipe mode streams data directly from S3 to the training algorithm without writing it to disk, eliminating the I/O bottleneck of downloading the full 100 GB dataset. This reduces training time significantly by overlapping data loading with computation, while preserving model accuracy since the entire dataset is still used.
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 instances to avoid communication overhead.
Why it's wrong here
Using fewer instances increases training time.
- ✓
Use Pipe mode to stream data from S3 instead of downloading it first.
Why this is correct
Pipe mode reduces I/O time by streaming data directly to the algorithm.
- ✗
Use random sampling to reduce the dataset size to 10 GB.
Why it's wrong here
Reducing dataset size may sacrifice accuracy.
- ✗
Use SageMaker Managed Spot Training to reduce cost, but training time may increase due to interruptions.
Why it's wrong here
Spot instances can reduce cost but not necessarily training time; interruptions can increase time.
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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