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Question 33 - MLS-C01 discussion

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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.
Answers
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.
Answers
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.
Answers
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.
Answers
D.
Increase the cooldown period for the scale-out activity.
Suggested answer: D

Explanation:

: The correct solution for changing the scaling behavior of the SageMaker instances is to increase the cooldown period for the scale-out activity. The cooldown period is the amount of time, in seconds, after a scaling activity completes before another scaling activity can start. By increasing the cooldown period for the scale-out activity, the ML team can ensure that the new instances are ready before launching additional instances.This will prevent over-scaling and reduce costs1

The other options are incorrect because they either do not solve the issue or require unnecessary steps. For example:

Option A decreases the cooldown period for the scale-in activity and increases the configured maximum capacity of instances. This option does not address the issue of launching additional instances before the new instances are ready. It may also cause under-scaling and performance degradation.

Option B replaces the current endpoint with a multi-model endpoint using SageMaker. A multi-model endpoint is an endpoint that can host multiple models using a single endpoint. It does not affect the scaling behavior of the SageMaker instances.It also requires creating a new endpoint and updating the application code to use it2

Option C sets up Amazon API Gateway and AWS Lambda to trigger the SageMaker inference endpoint. Amazon API Gateway is a service that allows users to create, publish, maintain, monitor, and secure APIs. AWS Lambda is a service that lets users run code without provisioning or managing servers. These services do not affect the scaling behavior of the SageMaker instances.They also require creating and configuring additional resources and services34

References:

1:Automatic Scaling - Amazon SageMaker

2:Create a Multi-Model Endpoint - Amazon SageMaker

3:Amazon API Gateway - Amazon Web Services

4:AWS Lambda - Amazon Web Services

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asked 16/09/2024
Srinivasan Krishnamoorthy
39 questions
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