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Linux Foundation KCNA Exam Syllabus Topics:

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

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Linux Foundation Kubernetes and Cloud Native Associate Sample Questions (Q31-Q36):

NEW QUESTION # 31
Which key-value store is used to persist Kubernetes cluster data?

Answer: A


NEW QUESTION # 32
What does "continuous" mean in the context of CI/CD?

Answer: C

Explanation:
The correct answer is C: in CI/CD, "continuous" implies frequent releases, automation, repeatability, and fast feedback/processing. The intent is to reduce batch size and latency between code change and validation
/deployment. Instead of integrating or releasing in large, risky chunks, teams integrate changes continually and rely on automation to validate and deliver them safely.
"Continuous" does not mean "periodic" (which eliminates B and D). It also does not mean "manual processes" (which eliminates A and B). Automation is core: build, test, security checks, and deployment steps are consistently executed by pipeline systems, producing reliable outcomes and auditability.
In practice, CI means every merge triggers automated builds and tests so the main branch stays in a healthy state. CD means those validated artifacts are promoted through environments with minimal manual steps, often including progressive delivery controls (canary, blue/green), automated rollbacks on health signal failures, and policy checks. Kubernetes works well with CI/CD because it is declarative and supports rollout primitives: Deployments, readiness probes, and rollback revision history enable safer continuous delivery when paired with pipeline automation.
Repeatability is a major part of "continuous." The same pipeline should run the same way every time, producing consistent artifacts and deployments. This reduces "works on my machine" issues and shortens incident resolution because changes are traceable and reproducible. Fast processing and frequent releases also mean smaller diffs, easier debugging, and quicker customer value delivery.
So, the combination that accurately reflects "continuous" in CI/CD is frequent + automated + repeatable + fast, which is option C.
=========


NEW QUESTION # 33
What are container runtimes with Kubernetes?

Answer: A,B

Explanation:
https://kubernetes.io/docs/setup/production-environment/container-runtimes/


NEW QUESTION # 34
What is the name for a service that has no clusterIp address?

Answer: D

Explanation:
https://kubernetes.io/docs/concepts/services-networking/service/#headless-services


NEW QUESTION # 35
How does dynamic storage provisioning work?

Answer: B

Explanation:
Dynamic provisioning is the Kubernetes mechanism where storage is created on-demand when a user creates a PersistentVolumeClaim (PVC) that references a StorageClass, so A is correct. In this model, the user does not need to pre-create a PersistentVolume (PV). Instead, the StorageClass points to a provisioner (typically a CSI driver) that knows how to create a volume in the underlying storage system (cloud disk, SAN, NAS, etc.). When the PVC is created with storageClassName: <class>, Kubernetes triggers the provisioner to create a new volume and then binds the resulting PV to that PVC.
This is why option B is incorrect: you do not put a StorageClass "in the Pod YAML" to request provisioning.
Pods reference PVCs, not StorageClasses directly. Option C is incorrect because the PVC does not need the Pod name; binding is done via the PVC itself. Option D describes static provisioning: an admin pre-creates PVs and users claim them by creating PVCs that match the PV (capacity, access modes, selectors). Static provisioning can work, but it is not dynamic provisioning.
Under the hood, the StorageClass can define parameters like volume type, replication, encryption, and binding behavior (e.g., volumeBindingMode: WaitForFirstConsumer to delay provisioning until the Pod is scheduled, ensuring the volume is created in the correct zone). Reclaim policies (Delete/Retain) define what happens to the underlying volume after the PVC is deleted.
In cloud-native operations, dynamic provisioning is preferred because it improves developer self-service, reduces manual admin work, and makes scaling stateful workloads easier and faster. The essence is: PVC + StorageClass # automatic PV creation and binding.
=========


NEW QUESTION # 36
......

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