MLS-C01 Modeling Practice Question
A company uses Amazon SageMaker to train a deep learning model for image classification. The training dataset consists of 500,000 images, each 256x256 pixels, stored in S3. The team uses a single ml.p3.2xlarge instance for training. The training time is unacceptably long (over 48 hours). The team wants to reduce training time without sacrificing model accuracy. They have already optimized the data pipeline by using SageMaker Pipe mode and sharding the S3 dataset. The model is a ResNet-50 implemented in TensorFlow. The team is considering the following options: A) Switch to a ml.p3.16xlarge instance which has 8 GPUs and more memory. B) Implement distributed data parallelism using Horovod across multiple instances. C) Use SageMaker's built-in Hyperparameter Tuning to find optimal hyperparameters. D) Reduce the image resolution to 128x128 to speed up training. Which option will MOST effectively reduce training time while maintaining accuracy?
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
✓
Implement distributed data parallelism using Horovod across multiple instances
Using multiple instances with Horovod for distributed data parallelism can scale training linearly with the number of GPUs, significantly reducing time. A larger single instance (ml.p3.16xlarge) provides 8 GPUs but still limited by single instance. Hyperparameter tuning does not directly reduce training time. Reducing resolution may lose accuracy.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch to a ml.p3.16xlarge instance
Why it's wrong here
Larger instance provides more GPUs but still limited; distributed training across instances scales better.
- ✗
Reduce the image resolution to 128x128
Why it's wrong here
Reducing resolution may speed up training but likely reduces model accuracy.
- ✓
Implement distributed data parallelism using Horovod across multiple instances
Why this is correct
Horovod enables efficient multi-GPU, multi-instance training, scaling training time linearly.
- ✗
Use SageMaker's built-in Hyperparameter Tuning
Why it's wrong here
Hyperparameter tuning is for finding best hyperparameters, not for reducing training 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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