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Exam AWS Certified DevOps Engineer - Professional DOP-C02 topic 1 question 290 discussion

Exam question from Amazon's AWS Certified DevOps Engineer - Professional DOP-C02
Question #: 290
Topic #: 1
[All AWS Certified DevOps Engineer - Professional DOP-C02 Questions]

A DevOps team is deploying microservices for an application on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. The cluster uses managed node groups. The DevOps team wants to enable auto scaling for the microservice Pods based on a specific CPU utilization percentage. The DevOps team has already installed the Kubernetes Metrics Server on the cluster.

Which solution will meet these requirements in the MOST operationally efficient way?

  • A. Edit the Auto Scaling group that is associated with the worker nodes of the EKS cluster. Configure the Auto Scaling group to use a target tracking scaling policy to scale when the average CPU utilization of the Auto Scaling group 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 the target CPU utilization percentage. Configure the VPA to use the recommender mode setting.
  • C. Run the AWS Systems Manager AWS-UpdateEKSManagedNodeGroup Automation document. Modify the values for NodeGroupDesiredSize, NodeGroupMaxSize, and NodeGroupMinSize to be based on an estimate for the required node size.
  • D. Deploy the Kubernetes Horizontal Pod Autoscaler (HPA) and the Kubernetes Cluster Autoscaler in the cluster. Configure the HPA to scale based on the target CPU utilization percentage. Configure the Cluster Autoscaler to use the auto-discovery setting.
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Suggested Answer: D 🗳️
by Ky_24 at Dec. 15, 2024, 1:30 p.m.
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Ky_24
4 months, 1 week ago
Selected Answer: D
Why Option D is Correct: Horizontal Pod Autoscaler (HPA): HPA automatically adjusts the number of Pods in a Kubernetes Deployment, ReplicaSet, or StatefulSet based on observed CPU or memory utilization or custom metrics. With the Kubernetes Metrics Server already installed, HPA can monitor CPU utilization metrics and scale the Pods to maintain the target CPU usage percentage. Cluster Autoscaler: When HPA increases the number of Pods and there aren’t enough resources in the cluster, Cluster Autoscaler adds nodes to the managed node group dynamically. The auto-discovery feature enables Cluster Autoscaler to automatically detect the appropriate node group and scale it up or down as needed. Operational Efficiency: This combination ensures Pods scale first at the workload level (HPA) and then at the infrastructure level (Cluster Autoscaler) only if required. This approach minimizes cost and ensures optimal resource utilization.
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