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Linux Foundation KCNA Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Cloud Native Application Delivery8%- GitOps
  • 1. GitOps Principles and Workflow
  • 2. Tools (Argo CD, Flux)
- Deployment Strategies
  • 1. Blue/Green, Canary, Rolling Updates
- CI/CD
  • 1. Artifact Management and Image Registries
  • 2. Continuous Integration and Continuous Delivery Pipelines
Topic 2: Kubernetes Fundamentals46%- Kubernetes API
  • 1. Declarative Management (Manifests/YAML)
  • 2. API Resource Structure and Versioning
- Kubernetes Resources
  • 1. Configuration Resources (ConfigMaps, Secrets)
  • 2. Networking Resources (Services, Ingress)
  • 3. Workload Resources (Pods, Deployments, StatefulSets, DaemonSets, ReplicaSets, Jobs, CronJobs)
- Containers
  • 1. Basic kubectl Commands
  • 2. Container Runtime Interface (CRI)
  • 3. Container Images and Registries
- Scheduling
  • 1. Node Selection and Affinity
  • 2. Taints and Tolerations
  • 3. Resource Requests and Limits
- Kubernetes Architecture
  • 1. Control Plane Components (API Server, etcd, Scheduler, Controller Manager)
  • 2. Worker Node Components (Kubelet, Kube-proxy, Container Runtime)
Topic 3: Cloud Native Architecture16%- Cloud Native Landscape
  • 1. CNCF Project Categories (Sandbox, Incubating, Graduated)
  • 2. CNCF Role and Governance
- Infrastructure and Practices
  • 1. Infrastructure as Code (IaC)
  • 2. DevOps Practices and Culture
  • 3. Immutable Infrastructure
- Architecture Concepts
  • 1. Autoscaling (HPA, VPA, Cluster Autoscaler)
  • 2. Serverless and FaaS
  • 3. Microservices Architecture
  • 4. Elasticity and Resilience
Topic 4: Container Orchestration22%- Service Mesh
  • 1. Service Mesh Concepts (Istio, Linkerd)
  • 2. Sidecar Pattern and Traffic Management
- Networking
  • 1. Kubernetes Networking Model
  • 2. CoreDNS and Service Networking
- 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
- Security
  • 1. RBAC (Role-Based Access Control)
  • 2. Pod Security Standards (Admission Control)
  • 3. Network Policies
- Container Runtimes
  • 1. Docker, containerd, CRI-O
Topic 5: Cloud Native Observability8%- 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)

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KCNA Latest Exam Guide | Latest KCNA Test Labs

The aim of PracticeMaterial is to support you in passing the Linux Foundation KCNA certification exam. PracticeMaterial present actual Linux Foundation KCNA practice test questions for you. The world's skilled professionals share their best knowledge with PracticeMaterial and create this set of actual Kubernetes and Cloud Native Associate KCNA

Linux Foundation Kubernetes and Cloud Native Associate Sample Questions (Q342-Q347):

NEW QUESTION # 342
A ________ is an application running on kubernetes.

Answer: A

Explanation:
https://kubernetes.io/docs/concepts/workloads/


NEW QUESTION # 343
How many different Kubernetes service types can you define?

Answer: B


NEW QUESTION # 344
What happens if only a limit is specified for a resource and no admission-time mechanism has applied a default request?

Answer: A

Explanation:
In Kubernetes, resource management for containers is based on requests and limits. Requests represent the minimum amount of CPU or memory required for scheduling decisions, while limits define the maximum amount a container is allowed to consume at runtime. Understanding how Kubernetes behaves when only a limit is specified is important for predictable scheduling and resource utilization.
If a container specifies a resource limit but does not explicitly specify a resource request, Kubernetes applies a well-defined default behavior. In this case, Kubernetes automatically sets the request equal to the specified limit. This behavior ensures that the scheduler has a concrete request value to use when deciding where to place the Pod. Without a request value, the scheduler would not be able to make accurate placement decisions, as scheduling is entirely request-based.
This defaulting behavior applies independently to each resource type, such as CPU and memory. For example, if a container sets a memory limit of 512Mi but does not define a memory request, Kubernetes treats the memory request as 512Mi as well. The same applies to CPU limits. As a result, the Pod is scheduled as if it requires the full amount of resources defined by the limit.
Option A is incorrect because specifying only a limit does not cause a container to crash or enter CrashLoopBackOff. CrashLoopBackOff is related to application failures, not resource specification defaults.
Option B is incorrect because Kubernetes allows containers to be created without explicit requests, relying on defaulting behavior instead. Option D is incorrect because Kubernetes never assigns random values for resource requests.
This behavior is clearly defined in Kubernetes resource management documentation and is especially relevant when admission controllers like LimitRange are not applying default requests. While valid, relying solely on limits can reduce cluster efficiency, as Pods may reserve more resources than they actually need. Therefore, best practice is to explicitly define both requests and limits.
Thus, the correct and verified answer is Option C.


NEW QUESTION # 345
What is a key feature of a container network?

Answer: D


NEW QUESTION # 346
In Kubernetes Role-Based Access Control (RBAC), what is the primary difference between a Role and a ClusterRole?

Answer: D

Explanation:
A Role is scoped to a specific namespace and grants permissions only within that namespace, while a ClusterRole is cluster-wide and can grant permissions across all namespaces or for cluster-level resources.


NEW QUESTION # 347
......

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