Build a Practical Health, Scaling, and Scheduling Example in Kubernetes Fundamentals

Define the build target

For Health, Scaling, and Scheduling, deploy a small workload with configuration and readiness checks, inspect the resulting pods, change desired replicas, and observe the controller reconcile state. Build the boundary case using this implementation lens: Use Kubernetes API objects, Pods, controllers, Services, ConfigMaps/Secrets, volumes, probes, scheduling, RBAC, rollouts, events, and kubectl diagnostics.

Keep the Health, Scaling, and Scheduling build centered on these technical constraints: Control plane and reconciliation. Pod/Deployment workload model. Apply them through this path lens: Use Kubernetes API objects, Pods, controllers, Services, ConfigMaps/Secrets, volumes, probes, scheduling, RBAC, rollouts, events, and kubectl diagnostics. Use Kubernetes API objects, Pods, controllers, Services, ConfigMaps/Secrets, volumes, probes, scheduling, RBAC, rollouts, events, and kubectl diagnostics.

Implement the core behavior

Implement Health, Scaling, and Scheduling around the module artifact—a working health, scaling, and scheduling exercise with a documented technical result—and keep the implementation specific to this path context: Use Kubernetes API objects, Pods, controllers, Services, ConfigMaps/Secrets, volumes, probes, scheduling, RBAC, rollouts, events, and kubectl diagnostics.

apiVersion: apps/v1
kind: Deployment
metadata: {name: api}
spec:
  replicas: 2
  selector: {matchLabels: {app: api}}
  template:
    metadata: {labels: {app: api}}
    spec:
      containers:
        - name: api
          image: nginx:1.27
          ports: [{containerPort: 80}]
          readinessProbe:
            httpGet: {path: /, port: 80}
            periodSeconds: 5
          resources:
            requests: {cpu: 50m, memory: 32Mi}

kubectl apply -f deployment.yaml && kubectl get pods -l app=api

The Deployment controller creates two labeled pods and readiness determines whether they are eligible to receive traffic.

practice/\n├── README.md\n├── health-scaling-and-scheduling-build.yaml\n└── evidence/\n    └── expected-result.txt

Run the complete path

Run one realistic Health, Scaling, and Scheduling case end to end and record the required evidence: the module-specific command, output, test, rendered state, query result, log, or measurement that proves the exercise worked. Interpret the result through this path context: Use Kubernetes API objects, Pods, controllers, Services, ConfigMaps/Secrets, volumes, probes, scheduling, RBAC, rollouts, events, and kubectl diagnostics.

Change one meaningful condition

Modify one condition central to Health, Scaling, and Scheduling using this path context: Use Kubernetes API objects, Pods, controllers, Services, ConfigMaps/Secrets, volumes, probes, scheduling, RBAC, rollouts, events, and kubectl diagnostics. Predict the new result before rerunning the same workflow.

Verify the artifact

Your deliverable is a working health, scaling, and scheduling exercise with a documented technical result.

Common mistakes to avoid

• Selector does not match pod labels.
• Secret/config key missing.
• Readiness/liveness probe wrong.
• Resource requests or scheduling constraints prevent placement.