Deploy and manage application workloads with proper scheduling and configuration (15% of exam).
This domain covers understanding application deployments and how to perform rolling updates and rollbacks, using ConfigMaps and Secrets to configure applications, configuring workload autoscaling, understanding the primitives used to create robust self-healing application deployments, and configuring Pod admission and scheduling including limits and node affinity.
5 minutes
5 Questions
In the context of the Certified Kubernetes Administrator (CKA) exam, 'Workloads & Scheduling' represents a critical domain focusing on how applications are packaged, deployed, and assigned to specific nodes within the cluster.
**Workloads** refer to the Kubernetes controllers that manage the lifecycle of Pods. While a **Pod** is the smallest deployable unit, you are expected to manage them via higher-level abstractions. You must master **Deployments** for stateless applications, demonstrating the ability to perform rolling updates, rollbacks, and scaling operations. You must also understand **StatefulSets** for applications requiring persistent storage and stable network identities, **DaemonSets** for running system agents (like loggers) on every node, and **Jobs/CronJobs** for batch processing. Understanding how to use **InitContainers** for setup scripts is also required.
**Scheduling** controls how the Kubernetes Scheduler places these Pods onto Nodes. The CKA curriculum tests your ability to manipulate this placement logic extensively. At a basic level, you use **Labels** and **Selectors** to organize resources. For specific node selection, you utilize **Node Affinity** (preference or requirement rules based on node labels) and **Pod Affinity/Anti-Affinity** (rules based on other pods running on a node). A crucial concept is **Taints and Tolerations**; Taints are applied to nodes to repel pods, while Tolerations are applied to pods to allow them to schedule on tainted nodes (commonly used to dedicate nodes to specific teams or keep workloads off the control plane).
Finally, scheduling is heavily influenced by **Resource Requests and Limits**, which ensure nodes have sufficient CPU and Memory capacity, and **Static Pods**, which bypass the scheduler entirely and are managed directly by the Kubelet via files on the host node.In the context of the Certified Kubernetes Administrator (CKA) exam, 'Workloads & Scheduling' represents a critical domain focusing on how applications are packaged, deployed, and assigned to specific nodes within the cluster.
**Workloads** refer to the Kubernetes controllers that manage the lifec…