Q69 — AWS DOP-C02 Ch.2

Question 69 of 100 | ← Chapter 2

A development team is deploying microservices on Amazon Elastic Kubernetes Service (Amazon EKS) clusters. The clusters use managed node groups. The team wants to enable automatic scaling of microservice pods based on specific CPU utilization thresholds. The team has already installed the Kubernetes Metrics Server on the clusters. Which solution will meet these requirements in the most efficient way?

• A. Edit the Auto Scaling group associated with the EKS cluster's worker nodes. Configure the Auto Scaling group to scale using a target tracking scaling policy when the Auto Scaling group's CPU utilization reaches a specific percentage.
• B. Deploy the Kubernetes Horizontal Pod Autoscaler (HPA) and the Kubernetes Vertical Pod Autoscaler (VPA) in the cluster. Configure the HPA to scale based on target CPU utilization. Configure the VPA to run in recommender mode.
• C. Run the AWS Systems Manager Run Command automation document for the node group. Modify the node instance type, node labels, and node taints values based on estimated node size requirements.
• D. Deploy the Kubernetes Horizontal Pod Autoscaler (HPA) and the Kubernetes Cluster Autoscaler in the cluster. Configure the HPA to scale based on target CPU utilization. Configure the Cluster Autoscaler to use auto-discovery settings. ✓

Correct Answer: D. Deploy the Kubernetes Horizontal Pod Autoscaler (HPA) and the Kubernetes Cluster Autoscaler in the cluster. Configure the HPA to scale based on target CPU utilization. Configure the Cluster Autoscaler to use auto-discovery settings.

Explanation

Kubernetes autoscaling mechanisms integrate with Amazon EKS. The question addresses horizontal pod scaling and adjusting cluster node count for efficient resource management. The Horizontal Pod Autoscaler (HPA) dynamically adjusts the number of pod replicas based on CPU utilization, while the Cluster Autoscaler (CA) automatically scales node groups when nodes lack sufficient resources to schedule pods. AWS documentation recommends deploying both HPA and CA in EKS: HPA handles application-layer scaling, and CA ensures underlying node resources match pod scheduling needs. Option D correctly combines HPA and CA and enables auto-discovery so CA can identify EKS node groups; Option A directly manipulates the ASG, bypassing Kubernetes scheduling logic; Option B introduces VPA, which is irrelevant to the stated horizontal scaling requirement; Option C relies on external tooling rather than native Kubernetes functionality.