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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 138 discussion

A company has set up and deployed its machine learning (ML) model into production with an endpoint using Amazon SageMaker hosting services. The ML team has configured automatic scaling for its SageMaker instances to support workload changes. During testing, the team notices that additional instances are being launched before the new instances are ready. This behavior needs to change as soon as possible.
How can the ML team solve this issue?

  • A. Decrease the cooldown period for the scale-in activity. Increase the configured maximum capacity of instances.
  • B. Replace the current endpoint with a multi-model endpoint using SageMaker.
  • C. Set up Amazon API Gateway and AWS Lambda to trigger the SageMaker inference endpoint.
  • D. Increase the cooldown period for the scale-out activity.
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Suggested Answer: D 🗳️

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cron0001
Highly Voted 2 years ago
Selected Answer: D
I believe this is a problem to do with scaling out (increasing the number of instances), cooldown period should be increased. https://docs.aws.amazon.com/autoscaling/ec2/userguide/Cooldown.html
upvoted 14 times
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DimLam
Most Recent 6 months, 2 weeks ago
Selected Answer: D
https://aws.amazon.com/blogs/machine-learning/configuring-autoscaling-inference-endpoints-in-amazon-sagemaker/
upvoted 1 times
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Mickey321
8 months, 1 week ago
Selected Answer: D
Option D
upvoted 1 times
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AjoseO
1 year, 2 months ago
Selected Answer: D
The issue is related to scaling out, specifically the fact that new instances are being launched before the existing ones are ready. To address this issue, the ML team could consider increasing the minimum number of instances, reducing the target value for CPU utilization, or increasing the warm-up time for the instances. These actions can help to ensure that new instances are not launched until the existing ones have reached a stable state, which can prevent performance issues and ensure the reliability of the service.
upvoted 2 times
AjoseO
1 year, 2 months ago
Option D, which suggests increasing the cooldown period for the scale-out activity, could potentially help to address this issue by ensuring that the new instances are not launched too quickly. Option A, which suggests decreasing the cooldown period for the scale-in activity and increasing the maximum capacity of instances, is not an appropriate solution to the problem described. Decreasing the cooldown period for scale-in activity would result in instances being terminated too quickly, and increasing the maximum capacity of instances would not necessarily prevent new instances from being launched too quickly.
upvoted 2 times
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ystotest
1 year, 5 months ago
Selected Answer: D
Agreed with D. should be increased not decreased
upvoted 1 times
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ryuhei
1 year, 7 months ago
Selected Answer: D
Answer is "D"
upvoted 1 times
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SDikeman62
1 year, 11 months ago
Selected Answer: D
Definitely D.
upvoted 2 times
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