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Linux Foundation KCNA Exam Syllabus Topics:

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

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Linux Foundation Kubernetes and Cloud Native Associate Sample Questions (Q261-Q266):

NEW QUESTION # 261
What is the purpose of the kube-proxy?

Answer: D


NEW QUESTION # 262
You are implementing a continuous integration and continuous deployment (CI/CD) pipeline for a cloud native application running in Kubernetes. Which of the following tools can be used for building, testing, and deploying the application to the cluster?

Answer: A,B,C,D,E

Explanation:
All of the listed tools are widely used for CI/CD pipelines and can be integrated with Kubernetes. Each tool has its own strengths and weaknesses depending on your specific needs. Here's a brief overview: Jenkins: Open-source, highly customizable, and supports a wide range of plugins. CircleCl: Cloud-based, user-friendly, and well-suited for smaller teams and projects. Travis Cl: Also cloud-based, popular for open-source projects and known for its simple setup. GitHub Actions: Native to GitHub, allows you to build, test, and deploy directly within your repository. GitLab CI/CD: Integrated with GitLab, provides a complete CI/CD solution with features for building, testing, and deploying applications.


NEW QUESTION # 263
Which of the following is NOT a Kubernetes component?

Answer: D

Explanation:
Docker is not a Kubernetes component.


NEW QUESTION # 264
What is the core functionality of GitOps tools like Argo CD and Flux?

Answer: D

Explanation:
The defining capability of GitOps controllers such as Argo CD and Flux is continuous reconciliation: they compare the desired state stored in Git to the actual state in the cluster and then alert and/or correct drift, making D correct. In GitOps, Git becomes the single source of truth for declarative configuration (Kubernetes manifests, Helm charts, Kustomize overlays). The controller watches Git for changes and applies them, and it also watches the cluster for divergence.
This is more than "auditing human changes" (option A). GitOps does provide auditability because changes are made via commits and pull requests, but the core functionality is the reconciliation loop that keeps cluster state aligned with Git, including optional automated sync/remediation. Option B is not accurate because GitOps is not about tracking user Git commands; it's about reconciling desired state definitions. Option C (automatically creating pull requests for outdated dependencies) is a useful feature in some tooling ecosystems, but it is not the central defining behavior of GitOps controllers.
In Kubernetes delivery terms, this approach improves reliability: rollouts become repeatable, configuration drift is detected, and recovery is simpler (reapply known-good state from Git). It also supports separation of duties: platform teams can control policies and base layers, while app teams propose changes via PRs.
So the verified statement is: GitOps tools continuously reconcile Git desired state with cluster actual state-exactly option D.
________________________________________


NEW QUESTION # 265
What Kubernetes component is running on every worker node and makes sure containers are running in a Pod?

Answer: D

Explanation:
The kubelet runs on every worker node and communicates with the container runtime to ensure that the containers specified in each Pod are created, running, and healthy.


NEW QUESTION # 266
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