Certified Kubernetes Administrator (CKA)

Kubernetes outages rarely come from one bad command. They come from gaps in team knowledge: clusters no one knows how to upgrade, networking assumptions no one has validated, and workloads that fail silently because health checks were never configured. This CKA course gives IT teams a shared, hands-on baseline for running self-managed and cloud-hosted clusters.

Role fit: Assign this course to systems administrators, platform engineers, and DevOps practitioners who have at least a year of experience with orchestration infrastructure. Learners should already be comfortable with Linux and with Kubernetes basics such as using the kubectl command line. Newer team members may need introductory Kubernetes training first.

Time investment: The course runs about 17 hours across 18 skills. Each skill takes roughly 50 to 65 minutes, so a Team Lead can schedule one or two skills per week without pulling engineers off production work.

Risk and compliance angle: The course covers these core operational areas:

• Role-Based Access Control (RBAC) with Roles and RoleBindings
• Kubernetes networking concepts for understanding how cluster components and workloads communicate
• Highly available control planes and etcd clusters
• Version-to-version upgrades of kubeadm clusters

These are the practices that support uptime commitments and security reviews.

Change-management note: Demos use kubeadm for self-managed Kubernetes clusters and Amazon EKS for managed Kubernetes examples. Teams on other managed Kubernetes services will still find the core concepts transfer.

Training Managers can group these skills into Playlists that match the team's rollout plan. Team Reporting shows each engineer's progress toward CKA readiness.

Kubernetes problems rarely announce themselves. A deployment schedules 30 pods onto two small worker nodes and they never start. A cluster role gives more access than the team intended. A version upgrade has to happen while production workloads keep running. This course walks your team through these scenarios on real clusters built with kubeadm and Amazon EKS, so the first time they face them isn't during an outage.

• Faster root-cause analysis: Team members practice diagnosing resource exhaustion on worker nodes. They also use kubectl exec to open a shell inside a running pod, inspect log files, and debug a web server process in place.
• Safer releases: IT practitioners run blue-green deployments with multiple Deployments and Service selectors, and compare hard cutovers with progressive rollouts. Liveness and readiness probes catch unhealthy apps before users do.
• Reduced attack surface: Your team works with Kubernetes RBAC, including Roles and RoleBindings, to define what users can do inside a cluster and avoid giving broader access than needed.
• Less manual capacity work: Engineers see how managed Kubernetes services can automate control plane and worker node setup, and how cloud auto scaling groups can help clusters respond to changing demand.

The course also covers upgrading kubeadm clusters with workloads already running and building highly available control planes and etcd clusters. Together, these skills give your team the hands-on foundation to own cluster resilience internally.

Knowledge

• How kubeadm fits among Kubernetes deployment options, from Docker Desktop for local development to self-managed clusters on AWS EC2 and managed services like Amazon EKS
• How pod networking, a CNI such as Calico, CoreDNS service discovery, and Ingress controllers move and route traffic across control plane and worker nodes
• How RBAC Roles and RoleBindings control who can act on cluster resources
• How Deployments, ReplicaSets, and update strategies support rolling and blue-green releases
• How volumes, persistent volumes, storage classes, and snapshots provide storage for workloads
• How etcd and multiple control plane nodes support high availability

Ability

• Deploy, upgrade, and scale self-managed clusters with kubeadm, including highly available control planes and etcd clusters
• Write, validate, and apply YAML manifests with kubectl, including workload definitions that reference container images
• Maintain workload health and cluster capacity by inspecting Kubernetes resources, events, logs, and managed-service scaling behavior
• Restrict pod-to-pod and outbound traffic with network policy to protect sensitive services like databases
• Troubleshoot scheduling failures and resource exhaustion, and debug running pods with kubectl exec and log inspection