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Linux Foundation KCNA Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Container Orchestration22%- 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. Volumes, PersistentVolumes (PV), PersistentVolumeClaims (PVC)
  • 2. Storage Classes and Dynamic Provisioning
- Orchestration Fundamentals
  • 1. Scheduling and Resource Management
  • 2. Service Discovery and Load Balancing
  • 3. Self-healing and Rolling Updates
- Security
  • 1. RBAC (Role-Based Access Control)
  • 2. Pod Security Standards (Admission Control)
  • 3. Network Policies
- Container Runtimes
  • 1. Docker, containerd, CRI-O
Topic 2: Cloud Native Application Delivery8%- CI/CD
  • 1. Continuous Integration and Continuous Delivery Pipelines
  • 2. Artifact Management and Image Registries
- GitOps
  • 1. GitOps Principles and Workflow
  • 2. Tools (Argo CD, Flux)
- Deployment Strategies
  • 1. Blue/Green, Canary, Rolling Updates
Topic 3: Kubernetes Fundamentals46%- Scheduling
  • 1. Resource Requests and Limits
  • 2. Taints and Tolerations
  • 3. Node Selection and Affinity
- Containers
  • 1. Container Images and Registries
  • 2. Container Runtime Interface (CRI)
  • 3. Basic kubectl Commands
- Kubernetes Architecture
  • 1. Worker Node Components (Kubelet, Kube-proxy, Container Runtime)
  • 2. Control Plane Components (API Server, etcd, Scheduler, Controller Manager)
- Kubernetes API
  • 1. API Resource Structure and Versioning
  • 2. Declarative Management (Manifests/YAML)
- Kubernetes Resources
  • 1. Networking Resources (Services, Ingress)
  • 2. Configuration Resources (ConfigMaps, Secrets)
  • 3. Workload Resources (Pods, Deployments, StatefulSets, DaemonSets, ReplicaSets, Jobs, CronJobs)
Topic 4: Cloud Native Architecture16%- Cloud Native Landscape
  • 1. CNCF Role and Governance
  • 2. CNCF Project Categories (Sandbox, Incubating, Graduated)
- Architecture Concepts
  • 1. Serverless and FaaS
  • 2. Autoscaling (HPA, VPA, Cluster Autoscaler)
  • 3. Microservices Architecture
  • 4. Elasticity and Resilience
- Infrastructure and Practices
  • 1. DevOps Practices and Culture
  • 2. Immutable Infrastructure
  • 3. Infrastructure as Code (IaC)
Topic 5: 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)

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Linux Foundation Kubernetes and Cloud Native Associate Sample Questions (Q341-Q346):

NEW QUESTION # 341
In Kubernetes application delivery, what is the primary benefit of using GitOps tools like Argo CD?

Answer: C

Explanation:
GitOps tools such as Argo CD treat a version-controlled Git repository as the source of truth for the desired application state. They continuously compare that desired state with the Kubernetes cluster and synchronize differences.


NEW QUESTION # 342
A DevOps engineer is preparing worker-1 for maintenance. The node is running multiple Pods, but workloads must be safely evicted and rescheduled onto other nodes. Which kubectl command should the engineer use?

Answer: C

Explanation:
The kubectl drain command safely evicts Pods from a node and reschedules them onto other nodes while respecting policies, and the --ignore-daemonsets flag ensures DaemonSet-managed Pods are not blocked during the process.


NEW QUESTION # 343
In a cloud native world, what does the IaC abbreviation stands for?

Answer: A


NEW QUESTION # 344
Which Kubernetes resource workload ensures that all (or some) nodes run a copy of a Pod?

Answer: C

Explanation:
A DaemonSet is the workload controller that ensures a Pod runs on all nodes or on a selected subset of nodes, so A is correct. DaemonSets are used for node-level agents and infrastructure components that must be present everywhere-examples include log collectors, monitoring agents, storage daemons, CNI components, and node security tools.
The DaemonSet controller watches for node additions/removals. When a new node joins the cluster, Kubernetes automatically schedules a new DaemonSet Pod onto that node (subject to constraints such as node selectors, affinities, and taints/tolerations). When a node is removed, its DaemonSet Pod naturally disappears with it. This creates the "one per node" behavior that differentiates DaemonSets from other workload types.
A Deployment manages a replica count across the cluster, not "one per node." A StatefulSet manages stable identity and ordered operations for stateful replicas; it does not inherently map one Pod to every node. kubectl is a CLI tool and not a workload resource.
DaemonSets can also be scoped: by using node selectors, node affinity, and tolerations, you can ensure Pods run only on GPU nodes, only on Linux nodes, only in certain zones, or only on nodes with a particular label. That's why the question says "all (or some) nodes." Therefore, the correct and verified answer is DaemonSet (A).
________________________________________


NEW QUESTION # 345
How are ReplicaSets and Deployments related?

Answer: D

Explanation:
In Kubernetes, a Deployment is a higher-level controller that manages ReplicaSets, and ReplicaSets in turn manage Pods. That is exactly what option A states, making it the correct answer.
A ReplicaSet's job is straightforward: ensure that a specified number of Pod replicas matching a selector are running. It continuously reconciles actual state to desired state by creating new Pods when replicas are missing or removing Pods when there are too many. However, ReplicaSets alone do not provide the richer application rollout lifecycle features most teams need.
A Deployment adds those features by managing ReplicaSets across versions of your Pod template. When you update a Deployment (for example, change the container image tag), Kubernetes creates a new ReplicaSet with the new Pod template and then gradually scales the new ReplicaSet up and the old one down according to the Deployment strategy (RollingUpdate by default). Deployments also maintain rollout history, support rollback (kubectl rollout undo), and allow pause/resume of rollouts. This is why the common guidance is: you almost always create Deployments rather than ReplicaSets directly for stateless apps.
Option B is incorrect because stateful workloads are typically handled by StatefulSets, not ReplicaSets.
Deployments can run stateless apps, but ReplicaSets are also used under Deployments and are not "for stateful only." Option C is reversed: ReplicaSets are not "instances" of Deployments; Deployments create
/manage ReplicaSets. Option D is incorrect because Jobs/CronJobs are separate controllers for run-to- completion workloads and do not define ReplicaSets as subsets.
So the accurate relationship is: Deployment # manages ReplicaSets # which manage Pods, enabling declarative updates and controlled rollouts.
=========


NEW QUESTION # 346
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