MLS-C01 · Question #253
MLS-C01 Question #253: Real Exam Question with Answer & Explanation
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Question
A data scientist is training a large PyTorch model by using Amazon SageMaker. It takes 10 hours on average to train the model on GPU instances. The data scientist suspects that training is not converging and that resource utilization is not optimal. What should the data scientist do to identify and address training issues with the LEAST development effort?
Options
- AUse CPU utilization metrics that are captured in Amazon CloudWatch. Configure a CloudWatch
- BUse high-resolution custom metrics that are captured in Amazon CloudWatch. Configure an AWS
- CUse the SageMaker Debugger vanishing_gradient and LowGPUUtilization built-in rules to detect
- DUse the SageMaker Debugger confusion and feature_importance_overweight built-in rules to
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