A company runs container applications on Amazon Elastic Kubernetes Service (Amazon EKS). The workload varies throughout the day. The company wants Amazon EKS to scale in and out with the workload. Which combination of steps will meet these requirements with the LEAST operational overhead? (Choose two.)

To handle varying workloads with least operational overhead in Amazon EKS, horizontal pod autoscaling (HPA) and cluster autoscaling are the standard, integrated solutions. The Kubernetes Metrics Server provides the necessary resource utilization metrics (like CPU and memory) for HPA to automatically adjust the number of pod replicas based on demand. The Kubernetes Cluster Autoscaler then automatically adjusts the number of worker nodes in the EKS cluster to accommodate the changing pod requirements, ensuring resources are available without manual intervention. Using an AWS Lambda function to resize the EKS cluster would require custom development and maintenance, increasing operational overhead. Amazon API Gateway and AWS App Mesh are not directly involved in scaling EKS compute resources; API Gateway is for managing API access, and App Mesh is for service mesh capabilities like traffic routing and observability.