MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company is using Amazon SageMaker to train a deep learning model for image classification. The training job is using a single p3.2xlarge instance and takes 10 hours. The data scientist wants to reduce training time using distributed training. Which SageMaker feature should be used?
⚠ Common exam trap
It's easy for candidates to confuse distributed data parallelism (which reduces time by adding more instances) with model parallelism (which handles large models but not necessarily faster training) or with cost-saving features like Spot Training that do not affect training duration.
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 the SageMaker distributed data parallelism library with multiple p3.2xlarge instances.
The goal is to reduce training time, and the SageMaker distributed data parallelism library is designed to split the mini-batch across multiple GPU instances, enabling synchronous or asynchronous gradient updates that scale near-linearly with the number of instances. By adding more p3.2xlarge instances, the effective throughput increases, directly reducing wall-clock training time for the image classification model.
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 the SageMaker distributed data parallelism library with multiple p3.2xlarge instances.
Why this is correct
Data parallelism divides the batch across GPUs and synchronizes gradients, scaling training.
- ✗
Use SageMaker Managed Spot Training to reduce cost, but training time remains the same.
Why it's wrong here
Spot instances may be interrupted, potentially increasing total training time.
- ✗
Use SageMaker Hyperparameter Tuning to find optimal hyperparameters faster.
Why it's wrong here
Hyperparameter Tuning runs multiple training jobs but does not reduce the time per job.
- ✗
Use the SageMaker distributed model parallelism library with a single p3dn.24xlarge instance.
Why it's wrong here
Model parallelism is for large models that don't fit on one GPU, not for speeding up training.
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