Who Should Take This
It is ideal for software developers, site reliability engineers, and cloud operators who have basic Linux or container experience but are new to Kubernetes. They seek a concise, practical foundation to confidently deploy, troubleshoot, and scale workloads using the platform’s core API resources.
What's Included in AccelaStudy® AI
Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
20 Activity Formats
Course Outline
63 learning goals
1
Architecture & Components
4 topics
Control Plane Components
- Identify the control plane components (API server, etcd, scheduler, controller manager) and describe the role each plays in cluster management
- Explain how the API server acts as the central communication hub and describe the declarative model where controllers reconcile desired state with actual state
- Analyze how etcd provides distributed consensus storage and evaluate the implications of etcd availability on cluster operations
Worker Node Components
- Describe the worker node components (kubelet, kube-proxy, container runtime) and explain how they collaborate to run and network containers
- Explain how the kubelet watches for Pod assignments from the API server and manages container lifecycle on its node
- Evaluate how the scheduler selects nodes for Pod placement based on resource requests, affinity rules, taints, and tolerations
Namespaces and Organization
- Describe the purpose of Kubernetes namespaces and list the default namespaces (default, kube-system, kube-public, kube-node-lease)
- Apply labels, selectors, and annotations to organize and filter Kubernetes resources and describe how label selectors drive service routing and workload scheduling
- Analyze how namespaces provide resource isolation for multi-team clusters and evaluate strategies for namespace design in shared environments
API Resources and YAML Manifests
- Describe the structure of Kubernetes YAML manifests including apiVersion, kind, metadata, and spec sections and explain how the API server validates resources
- Use kubectl api-resources and kubectl api-versions to discover available resource types, their short names, and supported API groups
2
Pods & Workloads
4 topics
Pod Fundamentals
- Describe what a Pod is as the smallest deployable unit in Kubernetes and explain why Pods can contain one or more tightly coupled containers
- Write Pod specification YAML defining containers, ports, environment variables, and resource requests/limits
- Configure liveness, readiness, and startup probes to define health checking behavior and explain how each probe type affects Pod lifecycle and traffic routing
- Analyze Pod lifecycle phases (Pending, Running, Succeeded, Failed, Unknown) and evaluate how restart policies (Always, OnFailure, Never) affect container recovery
Multi-Container Pod Patterns
- Describe the sidecar, ambassador, and adapter multi-container Pod patterns and explain the use case and communication model for each
- Configure init containers to perform setup tasks (database migrations, config downloads, permission changes) before main containers start
- Evaluate when to use multi-container Pods versus separate Pods and analyze the coupling and lifecycle implications of co-located containers
Deployments and ReplicaSets
- Describe how ReplicaSets ensure a specified number of Pod replicas are running and explain why Deployments are preferred over direct ReplicaSet management
- Create and update Deployments using kubectl and YAML manifests, specifying replicas, image versions, and update strategies
- Compare rolling update and recreate deployment strategies and evaluate the trade-offs in availability, resource usage, and rollback safety
- Use kubectl rollout commands to monitor deployment progress, pause updates, and roll back to previous revisions
- Configure Horizontal Pod Autoscaler (HPA) to automatically scale Deployment replicas based on CPU utilization or custom metrics
- Analyze the relationship between resource requests, HPA target utilization, and cluster capacity and evaluate scaling behavior under varying load patterns
Specialized Workload Controllers
- Describe the purpose of StatefulSets and explain how they provide stable network identities, ordered deployment, and persistent storage for stateful applications
- Describe DaemonSets and explain how they ensure exactly one Pod runs on every (or selected) node for cluster-wide agent deployment
- Use Jobs and CronJobs to run batch processing tasks and scheduled workloads and configure parallelism, completions, and failure thresholds
- Evaluate which workload controller (Deployment, StatefulSet, DaemonSet, Job) is appropriate for different application types based on statefulness, scheduling needs, and lifecycle requirements
3
Services & Networking
3 topics
Service Types
- Describe the Kubernetes Service abstraction and explain how Services provide stable endpoints for accessing a set of Pods selected by labels
- Compare ClusterIP, NodePort, LoadBalancer, and ExternalName Service types and describe the network accessibility and use case for each
- Create Service YAML manifests with appropriate selectors, ports, and type declarations to expose applications within and outside the cluster
Ingress and External Access
- Describe how Ingress resources provide HTTP/HTTPS routing, host-based and path-based routing, and TLS termination for external traffic
- Write Ingress resource manifests with path-based routing rules to direct traffic to multiple backend services through a single external endpoint
- Evaluate the trade-offs between using NodePort, LoadBalancer, and Ingress for exposing services externally based on cost, flexibility, and routing capabilities
DNS and Network Policies
- Explain how Kubernetes DNS enables service discovery using fully qualified domain names and describe the naming convention (service.namespace.svc.cluster.local)
- Define NetworkPolicy resources to control ingress and egress traffic between Pods based on labels, namespaces, and IP blocks
- Analyze how default-allow and default-deny network policies affect Pod communication and evaluate strategies for implementing least-privilege network access
4
Storage
3 topics
PersistentVolumes and Claims
- Describe the PersistentVolume (PV) and PersistentVolumeClaim (PVC) abstraction and explain how it decouples storage provisioning from storage consumption
- Create PersistentVolume and PersistentVolumeClaim resources and mount them to Pods using volumeMounts in the Pod specification
- Describe access modes (ReadWriteOnce, ReadOnlyMany, ReadWriteMany) and reclaim policies (Retain, Delete, Recycle) and explain how they constrain volume usage
StorageClasses and Dynamic Provisioning
- Describe how StorageClasses enable dynamic volume provisioning and explain the relationship between StorageClass parameters and the underlying storage backend
- Configure PVCs to request dynamic provisioning from a StorageClass and verify that volumes are created and bound correctly
- Evaluate the trade-offs between static and dynamic volume provisioning and analyze when each approach is appropriate based on operational requirements
Ephemeral and Projected Volumes
- Describe emptyDir volumes and explain how they provide temporary shared storage between containers in the same Pod that is deleted when the Pod terminates
- Use projected volumes to combine ConfigMap, Secret, and downward API data into a single volume mount for application consumption
5
Configuration & Secrets
3 topics
ConfigMaps
- Describe the purpose of ConfigMaps for externalizing application configuration and explain how they decouple configuration from container images
- Create ConfigMaps from literal values, files, and directories and consume them in Pods as environment variables or mounted volumes
- Analyze how ConfigMap updates propagate to running Pods and evaluate strategies for triggering application restarts when configuration changes
Secrets
- Describe the purpose of Kubernetes Secrets for storing sensitive data and explain how Secrets differ from ConfigMaps in encoding, access control, and intended usage
- Create Secrets for different use cases (Opaque, docker-registry, TLS) and mount them in Pods as environment variables or volume files
- Evaluate the security limitations of Kubernetes Secrets (base64 encoding is not encryption) and describe additional measures for protecting sensitive data at rest and in transit
Resource Management
- Define CPU and memory resource requests and limits in Pod specifications and explain how the scheduler uses requests for placement decisions
- Configure ResourceQuotas and LimitRanges at the namespace level to constrain aggregate resource consumption and enforce default limits
- Analyze the behavior when containers exceed memory limits (OOMKilled) versus CPU limits (throttled) and evaluate right-sizing strategies for resource allocation
6
Observability & Troubleshooting
3 topics
kubectl Essentials
- Use kubectl get, describe, and explain to inspect resource status, view detailed information, and look up API resource documentation
- Apply kubectl create, apply, edit, and delete to manage resource lifecycle using imperative commands and declarative YAML manifests
- Compare imperative commands, imperative object configuration, and declarative object configuration approaches and evaluate when each is appropriate
Logging and Events
- Retrieve container logs using kubectl logs with flags for following output, selecting containers in multi-container Pods, and viewing previous container logs
- Use kubectl get events and describe to examine cluster events and identify warnings, errors, and scheduling decisions that affect workload health
Debugging and Troubleshooting
- Use kubectl exec to run diagnostic commands inside containers and kubectl port-forward to access Pod services for local debugging
- Diagnose common Pod failure scenarios including ImagePullBackOff, CrashLoopBackOff, Pending state, and OOMKilled by interpreting status messages and events
- Apply a systematic troubleshooting methodology to diagnose networking, storage, and scheduling issues across Pods, Services, and Nodes
- Use kubectl top to view resource consumption of Pods and Nodes and evaluate whether current resource allocations match actual usage patterns
Hands-On Labs
15 labs
~375 min total
Console Simulator
Practice in a simulated cloud console or Python code sandbox — no account needed. Each lab runs entirely in your browser.
Scope
Included Topics
- Kubernetes architecture (control plane, worker nodes, etcd, API server, scheduler, controller manager, kubelet, kube-proxy), Pods and workload controllers (ReplicaSets, Deployments, StatefulSets, DaemonSets, Jobs, CronJobs), Services and networking (ClusterIP, NodePort, LoadBalancer, Ingress, network policies, DNS), persistent storage (PersistentVolumes, PersistentVolumeClaims, StorageClasses), configuration management (ConfigMaps, Secrets, environment variables), observability and troubleshooting (kubectl commands, logs, events, resource monitoring, debugging pods), namespaces and resource quotas, labels, selectors, and annotations
Not Covered
- Helm charts and package management
- Service mesh technologies (Istio, Linkerd)
- Cloud-specific managed Kubernetes (EKS, AKS, GKE) beyond awareness
- Custom Resource Definitions and Operators beyond awareness
- Advanced cluster administration (kubeadm, etcd backup, certificate management)
- CI/CD integration with Kubernetes beyond basic deployment concepts
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