MLS-C01 Modeling Practice Question
A data scientist is training a deep learning model on a large dataset using Amazon SageMaker. The training job is taking too long. The scientist notices that GPU utilization is low and data loading is the bottleneck. Which action should the scientist take to improve training performance?
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 for the training data channel
Low GPU utilization with a data loading bottleneck indicates that the CPU cannot feed data to the GPU quickly enough. SageMaker's Pipe mode streams training data directly from S3 without first downloading it to the instance's local storage, reducing I/O overhead and improving data throughput. Option A: Increasing the number of training instances does not address the per-instance data loading bottleneck; each GPU would still be underutilized. Option C: Changing to a CPU instance would be slower because CPUs are less efficient for deep learning training. Option D: Reducing the batch size would decrease GPU utilization further, exacerbating the underutilization problem.
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 number of training instances
Why it's wrong here
Increasing the number of training instances does not solve the per-instance data loading bottleneck; each instance would still suffer from low GPU utilization due to slow data loading.
- ✓
Use Pipe mode for the training data channel
Why this is correct
Pipe mode streams data directly from S3, reducing I/O overhead and allowing the GPU to receive data faster, thus improving utilization and training performance.
- ✗
Change the instance type to a CPU instance
Why it's wrong here
Changing to a CPU instance would be slower because CPUs are less efficient than GPUs for deep learning training, and the data loading bottleneck would still exist.
- ✗
Reduce the batch size
Why it's wrong here
Reducing the batch size would lower GPU utilization even further, making the training process slower and not addressing the data loading issue.
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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Senior Network & Security Engineer · founder of Courseiva
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