Topic 1 Question 40
A Machine Learning team uses Amazon SageMaker to train an Apache MXNet handwritten digit classifier model using a research dataset. The team wants to receive a notification when the model is overfitting. Auditors want to view the Amazon SageMaker log activity report to ensure there are no unauthorized API calls. What should the Machine Learning team do to address the requirements with the least amount of code and fewest steps?
Implement an AWS Lambda function to log Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
Implement an AWS Lambda function to log Amazon SageMaker API calls to AWS CloudTrail. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Set up Amazon SNS to receive a notification when the model is overfitting
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THE ANSWER SHOULD BE B. YOU DON'T NEED TO THROUGH LAMBDA TO INTERGE CLOUDTRAIL
Log Amazon SageMaker API Calls with AWS CloudTrail https://docs.aws.amazon.com/sagemaker/latest/dg/logging-using-cloudtrail.html
👍 40DonaldCMLIN2021/09/19Agreed B for the following reasons
CloudTrail logs captured in S3 without any code/lambda
The custom metrics can be published to Cloudwatch...in this case it would be a test for overfit on MXNET .... which will set off an alarm .... which can then be subscribed on SNS
👍 10rajs2021/09/25ALL AWS Service's API calls are logged to CloudTrail automatically.
👍 3ac4272021/09/25
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