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Linux Foundation KCNA Exam Syllabus Topics:

SectionWeightObjectives
Cloud Native Observability8%- Monitoring and Metrics
  • 1. Dashboards and Visualization (Grafana)
  • 2. Prometheus and Metrics Collection
- Logging
  • 1. Kubernetes Logging Architecture
  • 2. Centralized Logging (Fluentd, Elasticsearch, Kibana)
- Tracing
  • 1. Distributed Tracing Concepts (OpenTelemetry, Jaeger)
Cloud Native Application Delivery8%- Deployment Strategies
  • 1. Blue/Green, Canary, Rolling Updates
- GitOps
  • 1. Tools (Argo CD, Flux)
  • 2. GitOps Principles and Workflow
- CI/CD
  • 1. Continuous Integration and Continuous Delivery Pipelines
  • 2. Artifact Management and Image Registries
Container Orchestration22%- Orchestration Fundamentals
  • 1. Service Discovery and Load Balancing
  • 2. Scheduling and Resource Management
  • 3. Self-healing and Rolling Updates
- Storage
  • 1. Volumes, PersistentVolumes (PV), PersistentVolumeClaims (PVC)
  • 2. Storage Classes and Dynamic Provisioning
- Service Mesh
  • 1. Sidecar Pattern and Traffic Management
  • 2. Service Mesh Concepts (Istio, Linkerd)
- Container Runtimes
  • 1. Docker, containerd, CRI-O
- Security
  • 1. Network Policies
  • 2. Pod Security Standards (Admission Control)
  • 3. RBAC (Role-Based Access Control)
- Networking
  • 1. Kubernetes Networking Model
  • 2. CoreDNS and Service Networking
Kubernetes Fundamentals46%- Kubernetes Architecture
  • 1. Control Plane Components (API Server, etcd, Scheduler, Controller Manager)
  • 2. Worker Node Components (Kubelet, Kube-proxy, Container Runtime)
- Scheduling
  • 1. Taints and Tolerations
  • 2. Resource Requests and Limits
  • 3. Node Selection and Affinity
- Kubernetes Resources
  • 1. Networking Resources (Services, Ingress)
  • 2. Workload Resources (Pods, Deployments, StatefulSets, DaemonSets, ReplicaSets, Jobs, CronJobs)
  • 3. Configuration Resources (ConfigMaps, Secrets)
- Kubernetes API
  • 1. API Resource Structure and Versioning
  • 2. Declarative Management (Manifests/YAML)
- Containers
  • 1. Basic kubectl Commands
  • 2. Container Runtime Interface (CRI)
  • 3. Container Images and Registries
Cloud Native Architecture16%- Infrastructure and Practices
  • 1. DevOps Practices and Culture
  • 2. Infrastructure as Code (IaC)
  • 3. Immutable Infrastructure
- Cloud Native Landscape
  • 1. CNCF Project Categories (Sandbox, Incubating, Graduated)
  • 2. CNCF Role and Governance
- Architecture Concepts
  • 1. Autoscaling (HPA, VPA, Cluster Autoscaler)
  • 2. Elasticity and Resilience
  • 3. Serverless and FaaS
  • 4. Microservices Architecture

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Linux Foundation Kubernetes and Cloud Native Associate Sample Questions (Q61-Q66):

NEW QUESTION # 61
You're running a microservices application in Kubernetes, and you're concerned about the cost associated with resource usage. Which of the following approaches is most effective for reducing cost without impacting performance?

Answer: A,B,E

Explanation:
Cost optimization for microservices involves understanding resource consumption and ensuring efficient scaling. Distributed tracing helps pinpoint resource-intensive microservices for optimization. HPA allows individual microservices to scale independently, ensuring optimal resource utilization. Designing microservices to be stateless and idempotent simplifies scaling and restarts, reducing potential resource waste. While serverless functions offer scaling benefits, they might not be suitable for all microservices. Resource quotas can restrict usage but don't address dynamic scaling needs.


NEW QUESTION # 62
What do GitOps tools do in kubernetes?

Answer: B

Explanation:
https://fluxcd.io/docs/components/


NEW QUESTION # 63
How is application data maintained in containers?

Answer: B

Explanation:
Container filesystems are ephemeral: the writable layer is tied to the container lifecycle and can be lost when containers are recreated. Therefore, maintaining application data correctly means storing it in volumes, making D the correct answer. In Kubernetes, volumes provide durable or shareable storage that is mounted into containers at specific paths. Depending on the volume type, the data can persist across container restarts and even Pod rescheduling.
Kubernetes supports many volume patterns. For transient scratch data you might use emptyDir (ephemeral for the Pod's lifetime). For durable state, you typically use PersistentVolumes consumed by PersistentVolumeClaims (PVCs), backed by storage systems via CSI drivers (cloud disks, SAN/NAS, distributed storage). This decouples the application container image from its state and enables rolling updates, rescheduling, and scaling without losing data.
Options A and B ("folders") are incomplete because folders inside the container filesystem do not guarantee persistence. A folder is only as durable as the underlying storage; without a mounted volume, it lives in the container's writable layer and will disappear when the container is replaced. Option C is incorrect because
"sidecar containers" are not a data durability mechanism; sidecars can help ship logs or sync data, but persistent data should still be stored on volumes (or external services like managed databases).
From an application delivery standpoint, the principle is: containers should be immutable and disposable, and state should be externalized. Volumes (and external managed services) make this possible. In Kubernetes, this is a foundational pattern enabling safe rollouts, self-healing, and portability: the platform can kill and recreate Pods freely because data is maintained independently via volumes.
Therefore, the verified correct choice is D: Store data into volumes.
=========


NEW QUESTION # 64
What is the name of the Kubernetes resource used to expose an application?

Answer: C


NEW QUESTION # 65
Which control plane component is responsible for scheduling pods?

Answer: C

Explanation:
https://kubernetes.io/docs/concepts/overview/components/


NEW QUESTION # 66
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