Answer: Use the Kubernetes Metrics Server to activate horizontal pod autoscaling., Use the Kubernetes Cluster Autoscaler to manage the number of nodes in the cluster.

## Explanation To meet the requirements of scaling Amazon EKS according to workload with the **least operational overhead**, the correct combination is: **B. Use the Kubernetes Metrics Server to activate horizontal pod autoscaling.** **C. Use the Kubernetes Cluster Autoscaler to manage the number of nodes in the cluster.** ### Why this combination works: 1. **Horizontal Pod Autoscaling (HPA)** with Kubernetes Metrics Server: - HPA automatically scales the number of pods in a deployment based on observed CPU utilization or custom metrics - Kubernetes Metrics Server collects resource metrics from Kubelets and exposes them via the Metrics API - This handles scaling at the application/pod level based on actual workload 2. **Kubernetes Cluster Autoscaler:** - Automatically adjusts the size of the EKS cluster by adding or removing nodes - Works in conjunction with HPA - when pods can't be scheduled due to insufficient resources, Cluster Autoscaler adds nodes - When nodes are underutilized, it removes nodes to save costs - This handles scaling at the infrastructure/node level ### Why other options are incorrect: **A. Use an AWS Lambda function to resize the EKS cluster:** - This would require custom code, monitoring, and maintenance - Higher operational overhead compared to native Kubernetes solutions - Not the "least operational overhead" approach **D. Use Amazon API Gateway and connect it to Amazon EKS:** - API Gateway is for creating, publishing, and managing APIs - Not related to autoscaling EKS clusters - This would not help with scaling based on workload **E. Use AWS App Mesh to observe network activity:** - App Mesh is a service mesh for monitoring and controlling microservices - Provides observability for network traffic but doesn't handle autoscaling - Not designed for automatic scaling of EKS clusters ### Key Benefits of the Correct Solution: - **Native Kubernetes integration** - uses standard Kubernetes components - **Automated scaling** - both at pod and node levels without manual intervention - **Cost optimization** - scales down during low usage periods - **Minimal operational overhead** - managed by Kubernetes and AWS - **Seamless integration** - HPA and Cluster Autoscaler work together automatically This combination provides a complete autoscaling solution where HPA scales pods based on workload, and Cluster Autoscaler adjusts the node count to accommodate the pods, all with minimal operational effort.

Author: LeetQuiz Editorial Team