Google Cloud DevOps Certification Practice Test

The primary benefit of using Cluster Autoscaler in Google Kubernetes Engine (GKE) is its ability to dynamically adjust the size of the node pool based on the demands of workloads. This means that when there are more workloads requiring resources—for example, if additional Pods are scheduled or existing Pods need more resources—the Cluster Autoscaler can increase the number of nodes in the cluster to accommodate these needs. Conversely, when the workloads decrease, it can also reduce the number of nodes, helping to optimize resource utilization and cost.

This ability to respond to workload demands ensures that applications have the necessary resources available to run efficiently without manual intervention, which is particularly valuable in environments with fluctuating workloads. By automating the scaling process, organizations can improve operational efficiency and better manage costs associated with cloud resources.

In contrast, resizing Pods based on resource requests does not fall under the capabilities of Cluster Autoscaler, as that responsibility lies with Kubernetes's built-in features. Continuous monitoring of application performance metrics is related to other tools and practices, such as Prometheus or Google's Operations Suite. Lastly, simplifying service discovery pertains to how services within a Kubernetes cluster communicate with each other, which is handled by the Kubernetes networking model rather than the function of the Cluster Autoscaler.