MLS-C01 Modeling Practice Question
A company is training a deep learning model on Amazon SageMaker using a large dataset stored in S3. The training job is failing with an error indicating insufficient memory. The model architecture and hyperparameters are fixed. Which change is MOST likely to resolve the issue without modifying the model code?
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
✓
Enable SageMaker's distributed data parallelism.
Enable SageMaker's distributed data parallelism. Since the model architecture and hyperparameters are fixed, the insufficient memory error likely arises because the dataset is too large to fit into the memory of a single instance. Distributed data parallelism splits the training data across multiple instances, allowing each instance to process a smaller subset, thereby reducing per-instance memory usage without any code modifications. Option B (managed Spot training) reduces cost but does not address memory. Option C (using a larger instance) could provide more memory but may be more expensive and does not directly solve the root cause of data size relative to fixed hyperparameters. Option D (Pipe mode) improves data streaming efficiency but does not reduce the memory required for model parameters or intermediate activations.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable SageMaker's distributed data parallelism.
Why this is correct
Distributed data parallelism splits the minibatch across multiple GPUs/instances, reducing per-device memory footprint.
- ✗
Use managed Spot training to get cheaper compute.
Why it's wrong here
Spot instances provide lower cost but do not change the memory available to the training job.
- ✗
Use a larger instance type with more memory.
Why it's wrong here
While this may work, it does not address the root cause if the model requires more memory than available; also may increase cost unnecessarily.
- ✗
Use Pipe mode for input data instead of File mode.
Why it's wrong here
Pipe mode streams data directly to the algorithm, reducing local disk usage, but does not reduce memory consumption of the model.
Visual reference
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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