MLS-C01 Modeling Practice Question
A data scientist is using Amazon SageMaker to train a large language model with PyTorch. The training job is taking too long. The dataset is stored in S3 and the training script uses the SageMaker PyTorch container. Which change is MOST likely to reduce training time?
⚠ Common exam trap
A common mix-up: candidates confuse cost-saving measures (spot instances) or model-tuning changes (AdamW) with performance improvements, while the actual bottleneck in large-scale training is frequently data I/O, not compute or optimizer choice.
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.
SageMaker Pipe mode streams data directly from S3 to the training algorithm via a Unix FIFO (named pipe), eliminating the need to first download the entire dataset to the training instance's local storage. This reduces I/O wait time and disk usage, which is especially beneficial for large language models where dataset sizes can be in terabytes, thereby significantly cutting total training time.
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 instead of downloading.
Why this is correct
Pipe mode reduces data loading time.
- ✗
Increase the number of instances in the training job.
Why it's wrong here
Adding instances introduces communication overhead and may not linearly scale.
- ✗
Change the optimizer to AdamW.
Why it's wrong here
Optimizer choice has minimal impact on overall training time.
- ✗
Switch to spot instances to reduce cost.
Why it's wrong here
Spot instances do not speed up training and may cause interruptions.
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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