MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company is using Amazon SageMaker to train a model on a large dataset stored in S3. The training job is taking a long time due to slow data loading. Which action can the data scientist take to reduce data loading time?
⚠ Common exam trap
The MLS-C01 exam often tests the misconception that increasing instance size (Option C) solves all performance issues, but the trap here is that data loading latency is I/O-bound, not compute-bound, so Pipe mode directly mitigates the bottleneck by streaming instead of downloading.
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.
Pipe mode streams data directly from S3 into the training algorithm without first downloading it to the local storage, eliminating the bottleneck of disk I/O and reducing data loading time. This is especially effective for large datasets where the time to copy data to EBS (File mode) dominates the training job duration.
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 Pipe mode to stream data from S3.
Why this is correct
Pipe mode streams data directly, reducing load time.
- ✗
Use File mode and copy data to Amazon EBS.
Why it's wrong here
File mode downloads all data first, can be slow.
- ✗
Use a larger instance type with more memory.
Why it's wrong here
Larger instance doesn't speed up data loading from S3.
- ✗
Enable data augmentation during training.
Why it's wrong here
Augmentation adds overhead, doesn't speed loading.
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
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