Pre-Summer Sale Special Limited Time 65% Discount Offer - Ends in 0d 00h 00m 00s - Coupon code: ac4s65

An ML engineer is using a training job to fine-tune a deep learning model in...

Amazon Web Services MLA-C01 Full Course Access
Amazon Web Services MLA-C01 View All 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.

MLA-C01 PDF/Engine
  • Printable Format
  • Value of Money
  • 100% Pass Assurance
  • Verified Answers
  • Researched by Industry Experts
  • Based on Real Exams Scenarios
  • 100% Real Questions
buy now MLA-C01 pdf
Get 65% Discount on All Products, Use Coupon: "ac4s65"
Next
A company is using an ML model to classify motion in videos.
A company wants to use large language models (LLMs) supported by Amazon Bedrock to develop...
Previous