MLS-C01 · Question #229
MLS-C01 Question #229: Real Exam Question with Answer & Explanation
The correct answer is C: Use SageMaker Debugger for visibility into the training weights, gradients, biases, and activation. https://aws.amazon.com/blogs/machine-learning/pruning-machine-learning-models-with-amazon- sagemaker-debugger-and-amazon-sagemaker-experiments/
Question
An automotive company is using computer vision in its autonomous cars. The company has trained its models successfully by using transfer learning from a convolutional neural network (CNN). The models are trained with PyTorch through the use of the Amazon SageMaker SDK. The company wants to reduce the time that is required for performing inferences, given the low latency that is required for self-driving. Which solution should the company use to evaluate and improve the performance of the models?
Options
- AUse Amazon CloudWatch algorithm metrics for visibility into the SageMaker training weights,
- BUse SageMaker Debugger for visibility into the training weights, gradients, biases, and activation
- CUse SageMaker Debugger for visibility into the training weights, gradients, biases, and activation
- DUse SageMaker Model Monitor for visibility into the ModelLatency metric and OverheadLatency
Explanation
https://aws.amazon.com/blogs/machine-learning/pruning-machine-learning-models-with-amazon- sagemaker-debugger-and-amazon-sagemaker-experiments/
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