Explanation:
The Kubernetes Horizontal Pod Autoscaler (HPA) automatically updates a workload resource (such as a Deployment or StatefulSet) to match demand by scaling the number of pods out or in. Setting a target CPU utilization percentage enables HPA to dynamically increase the number of pods when CPU usage spikes due to high request volumes. This automated scaling natively solves the high traffic issue with the least administrative overhead. Vertical scaling (VPA) changes the resource limits of pods, which usually requires restarting pods and is less suitable for microservices designed for horizontal scaling. Managing multiple clusters or migrating to EC2 introduces significant unnecessary operational burden.
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Question 27. A company has implemented a Kubernetes cluster on Amazon Elastic Kubernetes Service (Amazon EKS) to host a microservices-based application. The company expects application traffic to increase significantly for the next month and wants to prevent the application from crashing because of the high number of requests. Which solution will meet these requirements with the LEAST administrative overhead?
A
Create a second EKS cluster. Load balance the workload between the two clusters.
B
Implement the Kubernetes Horizontal Pod Autoscaler. Set a target CPU utilization percentage.
C
Migrate the application from Amazon EKS to Amazon EC2 for the next month. Migrate the application back to Amazon EKS when the month ends.
D
Implement the Kubernetes Vertical Pod Autoscaler. Set a target CPU utilization percentage.
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