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Exam AWS Certified Machine Learning - Specialty All Questions

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Exam AWS Certified Machine Learning - Specialty topic 1 question 40 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty
Question #: 40
Topic #: 1
[All AWS Certified Machine Learning - Specialty Questions]

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?

  • A. 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.
  • B. 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.
  • C. 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.
  • D. 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
Show Suggested Answer Hide Answer
Suggested Answer: B 🗳️
by DonaldCMLIN at Nov. 16, 2019, 10:02 a.m.
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DonaldCMLIN
Highly Voted 3 years, 4 months ago
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
upvoted 41 times
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rajs
Highly Voted 3 years, 4 months ago
Agreed 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
upvoted 11 times
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MultiCloudIronMan
Most Recent 3 months ago
Selected Answer: B
Option D involves using AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3 and setting up Amazon SNS to receive a notification when the model is overfitting. While this approach addresses the logging requirement, it does not provide a mechanism for pushing custom metrics to Amazon CloudWatch, which is necessary for monitoring model performance and detecting overfitting. So 'B ' is correct
upvoted 2 times
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Chiquitabandita
9 months, 2 weeks ago
Selected Answer: D
https://docs.aws.amazon.com/fr_fr/sagemaker/latest/dg/training-metrics.html#define-train-metrics It detects hardware resource usage issues (such as CPU, GPU, and I/O bottlenecks) and non-convergent model issues (such as overfitting, disappearing gradients, and tensor explosion). why couldn't the answer be D, as this covers all of the requirements, and B seems to add an extra step with adding push code, when it already has a builtin metric for overfitting.
upvoted 1 times
Mobasher
4 days, 10 hours ago
This would have been correct had the question not mentioned that the algorithm is "hand-written" which means it's not a built in algorithm. So, for SageMaker AI to understand your custom algorithm's metrics, it needs a regex definition to apply to the logs in order to generate those custom metrics and then alert on them using CW Alarms and SNS to deliver notifications. See https://docs.aws.amazon.com/sagemaker/latest/dg/define-train-metrics.html
upvoted 1 times
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Aja1
9 months, 1 week ago
Custom metric Need to built and pushed.
upvoted 1 times
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loict
1 year, 4 months ago
Selected Answer: B
A. NO - CloudTrail has built-in SageMaker API calls tracking, no lambda needed B. YES - the chain works C. NO - CloudTrail has built-in SageMaker API calls tracking, no lambda needed D. NO - CloudTrail has not specific Amazon SageMaker integration to detect overfitting
upvoted 1 times
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Mickey321
1 year, 5 months ago
Selected Answer: B
Option B
upvoted 1 times
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ADVIT
1 year, 7 months ago
"least amount of code and fewest steps?" I think it's D.
upvoted 2 times
kukreti18
1 year, 6 months ago
Agreed, with less code effort.
upvoted 1 times
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Paolo991
1 year, 10 months ago
I would consider D as well. You can just setup a SNS that is triggered by a built-in action like here: https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-built-in-actions.html You can see that overfitting is a built-in rule for MXNet from here: https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-built-in-rules.html Not that B is not working. Maybe the question was prior to this new solution.
upvoted 2 times
khchan123
1 year, 2 months ago
The loss_not_decreasing, overfit, overtraining, and stalled_training_rule monitors if your model is optimizing the loss function without those training issues. If the rules detect training anomalies, the rule evaluation status changes to IssueFound. You can set up automated actions, such as notifying training issues and stopping training jobs using Amazon CloudWatch Events and AWS Lambda. For more information, see Action on Amazon SageMaker Debugger Rules. https://docs.aws.amazon.com/sagemaker/latest/dg/use-debugger-built-in-rules.html
upvoted 1 times
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Valcilio
1 year, 11 months ago
Selected Answer: B
It's B.
upvoted 1 times
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AjoseO
1 year, 11 months ago
Selected Answer: B
AWS CloudTrail provides a history of AWS API calls made on the account. The Machine Learning team can use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. They can then use CloudWatch to create alarms and receive notifications when the model is overfitting. To ensure auditors can view the Amazon SageMaker log activity report, the team can add code to push a custom metric to Amazon CloudWatch. This provides a single place to view and analyze logs across all the services and resources in the environment.
upvoted 1 times
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sonalev419
3 years, 3 months ago
B. cloudwatch + metrics from sagemaker + sns https://docs.aws.amazon.com/fr_fr/sagemaker/latest/dg/training-metrics.html#define-train-metrics
upvoted 4 times
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ybad
3 years, 3 months ago
B requires the least amount of code and satisfies all conditions
upvoted 2 times
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tochiebby
3 years, 3 months ago
What does this line do? "Add code to push a custom metric to Amazon CloudWatch"
upvoted 1 times
Omar_Cascudo
3 years, 3 months ago
It creates a metric for overfitting (accuracy of training data and accuracy of test data).
upvoted 5 times
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jonclem
3 years, 3 months ago
Its not B. Why would you use CloudTrail? Having used Lambda for API calls I'm inclined to agree with the original answer, C.
upvoted 1 times
Pja1
3 years, 3 months ago
https://docs.aws.amazon.com/sagemaker/latest/dg/logging-using-cloudtrail.html
upvoted 3 times
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fhuadeen
3 years, 3 months ago
Because that is the only job of CloudTrail - to log actions taken on your AWS account. So why need a Lambda function to trigger it?
upvoted 3 times
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Antriksh
3 years, 3 months ago
B it is
upvoted 2 times
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C10ud9
3 years, 3 months ago
B it is
upvoted 1 times
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lisiuyiu
3 years, 4 months ago
Agree on B
upvoted 2 times
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