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Linux Foundation KCNA Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Cloud Native Architecture16%- Infrastructure and Practices
  • 1. DevOps Practices and Culture
  • 2. Immutable Infrastructure
  • 3. Infrastructure as Code (IaC)
- Architecture Concepts
  • 1. Autoscaling (HPA, VPA, Cluster Autoscaler)
  • 2. Serverless and FaaS
  • 3. Elasticity and Resilience
  • 4. Microservices Architecture
- Cloud Native Landscape
  • 1. CNCF Project Categories (Sandbox, Incubating, Graduated)
  • 2. CNCF Role and Governance
Topic 2: Container Orchestration22%- Security
  • 1. Pod Security Standards (Admission Control)
  • 2. Network Policies
  • 3. RBAC (Role-Based Access Control)
- 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
- 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)
Topic 3: Cloud Native Observability8%- Logging
  • 1. Centralized Logging (Fluentd, Elasticsearch, Kibana)
  • 2. Kubernetes Logging Architecture
- Monitoring and Metrics
  • 1. Prometheus and Metrics Collection
  • 2. Dashboards and Visualization (Grafana)
- Tracing
  • 1. Distributed Tracing Concepts (OpenTelemetry, Jaeger)
Topic 4: Kubernetes Fundamentals46%- Kubernetes API
  • 1. Declarative Management (Manifests/YAML)
  • 2. API Resource Structure and Versioning
- Kubernetes Resources
  • 1. Workload Resources (Pods, Deployments, StatefulSets, DaemonSets, ReplicaSets, Jobs, CronJobs)
  • 2. Configuration Resources (ConfigMaps, Secrets)
  • 3. Networking Resources (Services, Ingress)
- 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. Node Selection and Affinity
  • 3. Resource Requests and Limits
- Containers
  • 1. Container Runtime Interface (CRI)
  • 2. Basic kubectl Commands
  • 3. Container Images and Registries
Topic 5: Cloud Native Application Delivery8%- 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
- Deployment Strategies
  • 1. Blue/Green, Canary, Rolling Updates

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Linux Foundation Kubernetes and Cloud Native Associate Sample Questions (Q26-Q31):

NEW QUESTION # 26
You have a Kubernetes cluster with multiple nodes, and you want to ensure that traffic is routed to pods based on labels. Which networking feature should you use?

Answer: D

Explanation:
The correct answer is 'Service of type 'ClusterlP' with selectors'. This approach allows you to define a Service with a 'ClusterlP' and specify a selector that matches pods with specific labels. Kubernetes will then route traffic to pods that have the matching labels. 'NetworkPolicy' is used for network access control, not for load balancing based on labels. 'Service of type 'LoadBalancer" is used for exposing services to the external network. 'Ingress resource' manages external traffic to the cluster 'Pod affinity and anti-affinity' are used for scheduling pods on specific nodes based on labels.


NEW QUESTION # 27
What is the primary reason for creating a Secret in Kubernetes instead of storing credentials in a ConfigMap?

Answer: C

Explanation:
A Secret is specifically intended for sensitive values such as passwords, API tokens, and certificates. It provides dedicated handling for confidential data and can be integrated with encryption at rest and access controls.


NEW QUESTION # 28
What is the default deployment strategy in Kubernetes?

Answer: B

Explanation:
For Kubernetes Deployments, the default update strategy is RollingUpdate, which corresponds to "Rolling update" in option A. Rolling updates replace old Pods with new Pods gradually, aiming to maintain availability during the rollout. Kubernetes does this by creating a new ReplicaSet for the updated Pod template and then scaling the new ReplicaSet up while scaling the old one down.
The pace and safety of a rolling update are controlled by parameters like maxUnavailable and maxSurge. maxUnavailable limits how many replicas can be unavailable during the update, protecting availability. maxSurge controls how many extra replicas can be created temporarily above the desired count, helping speed up rollouts while maintaining capacity. If readiness probes fail, Kubernetes will pause progression because new Pods aren't becoming Ready, helping prevent a bad version from fully replacing a good one.
Options B (Blue/Green) and C (Canary) are popular progressive delivery patterns, but they are not the default built-in Deployment strategy. They are typically implemented using additional tooling (service mesh routing, traffic splitting controllers, or specialized rollout controllers) or by operating multiple Deployments/Services. Option D (Recreate) is a valid strategy but not the default; it terminates all old Pods before creating new ones, causing downtime unless you have external buffering or multi-tier redundancy.
From an application delivery perspective, RollingUpdate aligns with Kubernetes' declarative model: you update the desired Pod template and let the controller converge safely. kubectl rollout status is commonly used to monitor progress. Rollbacks are also supported because the Deployment tracks history. Therefore, the verified correct answer is A: Rolling update.


NEW QUESTION # 29
How long should a stable API element in Kubernetes be supported (at minimum) after deprecation?

Answer: B

Explanation:
Kubernetes has a formal API deprecation policy to balance stability for users with the ability to evolve the platform. For a stable (GA) API element, Kubernetes commits to supporting that API for a minimum period after it is deprecated. The correct minimum in this question is 12 months, which corresponds to option C.
In practice, Kubernetes releases occur roughly every three to four months, and the deprecation policy is commonly communicated in terms of "releases" as well as time. A GA API that is deprecated in one release is typically kept available for multiple subsequent releases, giving cluster operators and application teams time to migrate manifests, client libraries, controllers, and automation. This matters because Kubernetes is often at the center of production delivery pipelines; abrupt API removals would break deployments, upgrades, and tooling. By guaranteeing a minimum support window, Kubernetes enables predictable upgrades and safer lifecycle management.
This policy also encourages teams to track API versions and plan migrations. For example, workloads might start on a beta API (which can change), but once an API reaches stable, users can expect a stronger compatibility promise. Deprecation warnings help surface risk early. In many clusters, you'll see API server warnings and tooling hints when manifests use deprecated fields/versions, allowing proactive remediation before the removal release.
Options 6 or 9 months would be too short for many enterprises to coordinate changes across multiple teams and environments. 24 months may be true for some ecosystems, but the Kubernetes stated minimum in this exam-style framing is 12 months. The key operational takeaway is: don't ignore deprecation notices-they' re your clock for migration planning. Treat API version upgrades as part of routine cluster lifecycle hygiene to avoid being blocked during Kubernetes version upgrades when deprecated APIs are finally removed.
=========


NEW QUESTION # 30
You are developing a microservices application where each service requires a specific configuration. Which Kubernetes feature best addresses this need for service-specific configuration?

Answer: D

Explanation:
ConfigMaps allow you to store key-value pairs of configuration data, which can be easily mounted as environment variables or files within your containers. This is ideal for handling service-specific configurations.


NEW QUESTION # 31
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