An operations team manages a stateless, fault-tolerant processing service deployed on a Google Kubernetes Engine (GKE) Standard cluster. During peak events, high application traffic causes existing pods to consume maximum allocated CPU resources, while new incoming pods remain in a Pending state due to insufficient cluster compute capacity. The team wants to ensure dynamic scalability at both the application level and infrastructure level while optimizing compute costs. Which TWO management strategies should the team implement? (Select TWO answers.)
- Configure a Horizontal Pod Autoscaler (HPA) targeting the Deployment workload to dynamically scale the number of pod replicas based on CPU utilization metrics.Cevap
- Enable the GKE Cluster Autoscaler on a node pool configured with Spot VMs to dynamically add node capacity when unschedulable pods are detected.Cevap
- CConfigure a Horizontal Pod Autoscaler (HPA) to automatically provision new Compute Engine worker nodes whenever pod memory request limits are exceeded.
- DMigrate the cluster to GKE Autopilot mode to enable manual tuning of custom host operating system kernel parameters and sysctl settings on worker nodes.
Cevap
The correct operational strategies are to configure a Horizontal Pod Autoscaler (HPA) to scale pod replicas based on workload metrics and enable the GKE Cluster Autoscaler on a Spot VM node pool to automatically provision worker nodes when pending pods cannot be scheduled.
Deploying a Horizontal Pod Autoscaler (HPA) addresses application load by dynamically increasing or decreasing pod replica counts according to metric thresholds. Enabling GKE Cluster Autoscaler on a node pool consisting of Spot VMs addresses cluster capacity limits by adding infrastructure when unschedulable pods exist, while minimizing compute expenditures for stateless workloads.
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Anahtar Kavram
Distinguishing between Horizontal Pod Autoscaler (HPA) for scaling pod workload replicas and GKE Cluster Autoscaler for dynamic node pool capacity adjustment.