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Linux Foundation KCNA Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Kubernetes Fundamentals	46%	- Containers 1. Container Runtime Interface (CRI) 2. Container Images and Registries 3. Basic kubectl Commands - Kubernetes Architecture 1. Control Plane Components (API Server, etcd, Scheduler, Controller Manager) 2. Worker Node Components (Kubelet, Kube-proxy, Container Runtime) - Kubernetes API 1. API Resource Structure and Versioning 2. Declarative Management (Manifests/YAML) - Scheduling 1. Node Selection and Affinity 2. Taints and Tolerations 3. Resource Requests and Limits - Kubernetes Resources 1. Networking Resources (Services, Ingress) 2. Workload Resources (Pods, Deployments, StatefulSets, DaemonSets, ReplicaSets, Jobs, CronJobs) 3. Configuration Resources (ConfigMaps, Secrets)
Topic 2: Cloud Native Architecture	16%	- Infrastructure and Practices 1. DevOps Practices and Culture 2. Immutable Infrastructure 3. Infrastructure as Code (IaC) - Architecture Concepts 1. Autoscaling (HPA, VPA, Cluster Autoscaler) 2. Serverless and FaaS 3. Elasticity and Resilience 4. Microservices Architecture - Cloud Native Landscape 1. CNCF Project Categories (Sandbox, Incubating, Graduated) 2. CNCF Role and Governance
Topic 3: Cloud Native Observability	8%	- Logging 1. Centralized Logging (Fluentd, Elasticsearch, Kibana) 2. Kubernetes Logging Architecture - Tracing 1. Distributed Tracing Concepts (OpenTelemetry, Jaeger) - Monitoring and Metrics 1. Prometheus and Metrics Collection 2. Dashboards and Visualization (Grafana)
Topic 4: Cloud Native Application Delivery	8%	- Deployment Strategies 1. Blue/Green, Canary, Rolling Updates - CI/CD 1. Artifact Management and Image Registries 2. Continuous Integration and Continuous Delivery Pipelines - GitOps 1. Tools (Argo CD, Flux) 2. GitOps Principles and Workflow
Topic 5: Container Orchestration	22%	- Container Runtimes 1. Docker, containerd, CRI-O - Service Mesh 1. Sidecar Pattern and Traffic Management 2. Service Mesh Concepts (Istio, Linkerd) - Networking 1. CoreDNS and Service Networking 2. Kubernetes Networking Model - Storage 1. Storage Classes and Dynamic Provisioning 2. Volumes, PersistentVolumes (PV), PersistentVolumeClaims (PVC) - Security 1. RBAC (Role-Based Access Control) 2. Pod Security Standards (Admission Control) 3. Network Policies - Orchestration Fundamentals 1. Service Discovery and Load Balancing 2. Scheduling and Resource Management 3. Self-healing and Rolling Updates

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Linux Foundation Kubernetes and Cloud Native Associate Sample Questions (Q206-Q211):

NEW QUESTION # 206
In a serverless computing architecture:

A. Serverless functions are incompatible with containerized functions.
B. Users of the cloud provider are charged based on the number of requests to a function.
C. Users should make a reservation to the cloud provider based on an estimation of usage.
D. Containers serving requests are running in the background in idle status.

Answer: B

NEW QUESTION # 207
Which of the following is the correct command to run a nginx deployment with 2 replicas?

A. kubectl create deploy nginx --image=nginx --replicas=2
B. kubectl run deploy nginx --image=nginx --replicas=2
C. kubectl create nginx deployment --image=nginx -replicas=2
D. kubectl create deploy nginx --image=nginx --count=2

Answer: A

NEW QUESTION # 208
A platform engineer wants to ensure that a new microservice is automatically deployed to every cluster registered in Argo CD. Which configuration best achieves this goal?

A. Set up a Kubernetes CronJob that redeploys the microservice to all registered clusters on a schedule.
B. Create an Argo CD ApplicationSet that uses a Git repository containing the microservice manifests.
C. Manually configure every registered cluster with the deployment YAML for installing the microservice.
D. Use a Helm chart to package the microservice and manage it with a single Application defined in ArgoCD.

Answer: B

Explanation:
An Argo CD ApplicationSet can dynamically generate Applications for all registered clusters based on a shared configuration, ensuring the microservice is automatically deployed and kept in sync across every cluster without manual intervention.

NEW QUESTION # 209
CI/CD stands for:

A. Cloud Integration / Cloud Development
B. Continuous Information / Continuous Development
C. Continuous Integration / Continuous Development
D. Continuous Integration / Continuous Deployment

Answer: D

Explanation:
CI/CD is a foundational practice for delivering software rapidly and reliably, and it maps strongly to cloud native delivery workflows commonly used with Kubernetes. CI stands for Continuous Integration: developers merge code changes frequently into a shared repository, and automated systems build and test those changes to detect issues early. CD is commonly used to mean Continuous Delivery or Continuous Deployment depending on how far automation goes. In many certification contexts and simplified definitions like this question, CD is interpreted as Continuous Deployment, meaning every change that passes the automated pipeline is automatically released to production. That matches option D.
In a Kubernetes context, CI typically produces artifacts such as container images (built from Dockerfiles or similar build definitions), runs unit/integration tests, scans dependencies, and pushes images to a registry. CD then promotes those images into environments by updating Kubernetes manifests (Deployments, Helm charts, Kustomize overlays, etc.). Progressive delivery patterns (rolling updates, canary, blue/green) often use Kubernetes-native controllers and Service routing to reduce risk.
Why the other options are incorrect: "Continuous Development" isn't the standard "D" term; it's ambiguous and not the established acronym expansion. "Cloud Integration/Cloud Development" is unrelated. Continuous Delivery (in the stricter sense) means changes are always in a deployable state and releases may still require a manual approval step, while Continuous Deployment removes that final manual gate. But because the option set explicitly includes "Continuous Deployment," and that is one of the accepted canonical expansions for CD, D is the correct selection here.
Practically, CI/CD complements Kubernetes' declarative model: pipelines update desired state (Git or manifests), and Kubernetes reconciles it. This combination enables frequent releases, repeatability, reduced human error, and faster recovery through automated rollbacks and controlled rollout strategies.

NEW QUESTION # 210
You are troubleshooting a performance issue with an application running in a Kubernetes cluster. You have access to Prometheus and Jaeger, but the Jaeger Ul shows that a specific request took an unusually long time to process. Which metric in Prometheus can help you identify the specific component or service within the application that caused the delay?

A. kube_pod_container_resource_requests_cpu_cores
B. kube_pod_container_status_restart_count
C. kube_deployment_status_replicas
D. http_request_duration_seconds
E. kube_pod_status_phase

Answer: D

Explanation:
The correct answer is E . The metric 'http_request_duration_seconds* is typically used to track the time taken for HTTP requests to be processed. In this case, by looking at the 'http_request_duration_seconds' metric for the specific request identified in Jaeger, you can see the duration of each stage of the request processing. This can pinpoint the specific component or service where the delay occurred. The other options are not relevant to identifying the specific component or service causing the delay: A : Shows CPU resource requests for a pod. B : Shows the pod's phase. C : Shows the number of replicas in a deployment. D : Shows the restart count for a pod's containers. These metrics do not provide information about the processing time of individual requests.

NEW QUESTION # 211
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