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.

Question

Quizplus · Accepted Answer

The Answer of 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.

A Company Has Set Up and Deployed Its Machine Learning