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Exam AWS Certified Machine Learning Engineer - Associate MLA-C01 All Questions

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Exam AWS Certified Machine Learning Engineer - Associate MLA-C01 topic 1 question 48 discussion

Exam question from Amazon's AWS Certified Machine Learning Engineer - Associate MLA-C01
Question #: 48
Topic #: 1
[All AWS Certified Machine Learning Engineer - Associate MLA-C01 Questions]

An ML engineer is using a training job to fine-tune a deep learning model in Amazon SageMaker Studio. The ML engineer previously used the same pre-trained model with a similar dataset. The ML engineer expects vanishing gradient, underutilized GPU, and overfitting problems.
The ML engineer needs to implement a solution to detect these issues and to react in predefined ways when the issues occur. The solution also must provide comprehensive real-time metrics during the training.
Which solution will meet these requirements with the LEAST operational overhead?

  • A. Use TensorBoard to monitor the training job. Publish the findings to an Amazon Simple Notification Service (Amazon SNS) topic. Create an AWS Lambda function to consume the findings and to initiate the predefined actions.
  • B. Use Amazon CloudWatch default metrics to gain insights about the training job. Use the metrics to invoke an AWS Lambda function to initiate the predefined actions.
  • C. Expand the metrics in Amazon CloudWatch to include the gradients in each training step. Use the metrics to invoke an AWS Lambda function to initiate the predefined actions.
  • D. Use SageMaker Debugger built-in rules to monitor the training job. Configure the rules to initiate the predefined actions.
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Suggested Answer: D 🗳️
by GiorgioGss at Nov. 27, 2024, 10:56 p.m.
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Saransundar
1 month ago
Selected Answer: D
SageMaker Debugger → Built-in rules → Monitor training (vanishing gradients, GPU use, overfitting) → Predefined actions → Low overhead
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GiorgioGss
1 month, 1 week ago
Selected Answer: D
https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-built-in-rules.html
upvoted 2 times
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