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| Section | Weight | Objectives |
|---|
| Topic 1: Cloud Native Application Delivery | 8% | - Deployment Strategies
- 1. Blue/Green, Canary, Rolling Updates
- GitOps
- 1. GitOps Principles and Workflow
- 2. Tools (Argo CD, Flux)
- CI/CD
- 1. Continuous Integration and Continuous Delivery Pipelines
- 2. Artifact Management and Image Registries
|
| Topic 2: Kubernetes Fundamentals | 46% | - Kubernetes API
- 1. API Resource Structure and Versioning
- 2. Declarative Management (Manifests/YAML)
- Kubernetes Architecture
- 1. Worker Node Components (Kubelet, Kube-proxy, Container Runtime)
- 2. Control Plane Components (API Server, etcd, Scheduler, Controller Manager)
- Scheduling
- 1. Taints and Tolerations
- 2. Node Selection and Affinity
- 3. Resource Requests and Limits
- Containers
- 1. Container Images and Registries
- 2. Container Runtime Interface (CRI)
- 3. Basic kubectl Commands
- Kubernetes Resources
- 1. Configuration Resources (ConfigMaps, Secrets)
- 2. Networking Resources (Services, Ingress)
- 3. Workload Resources (Pods, Deployments, StatefulSets, DaemonSets, ReplicaSets, Jobs, CronJobs)
|
| Topic 3: Container Orchestration | 22% | - Storage
- 1. Volumes, PersistentVolumes (PV), PersistentVolumeClaims (PVC)
- 2. Storage Classes and Dynamic Provisioning
- Security
- 1. Pod Security Standards (Admission Control)
- 2. Network Policies
- 3. RBAC (Role-Based Access Control)
- Networking
- 1. Kubernetes Networking Model
- 2. CoreDNS and Service Networking
- Service Mesh
- 1. Service Mesh Concepts (Istio, Linkerd)
- 2. Sidecar Pattern and Traffic Management
- Container Runtimes
- 1. Docker, containerd, CRI-O
- Orchestration Fundamentals
- 1. Self-healing and Rolling Updates
- 2. Service Discovery and Load Balancing
- 3. Scheduling and Resource Management
|
| Topic 4: Cloud Native Observability | 8% | - Logging
- 1. Kubernetes Logging Architecture
- 2. Centralized Logging (Fluentd, Elasticsearch, Kibana)
- Monitoring and Metrics
- 1. Dashboards and Visualization (Grafana)
- 2. Prometheus and Metrics Collection
- Tracing
- 1. Distributed Tracing Concepts (OpenTelemetry, Jaeger)
|
| Topic 5: Cloud Native Architecture | 16% | - Cloud Native Landscape
- 1. CNCF Project Categories (Sandbox, Incubating, Graduated)
- 2. CNCF Role and Governance
- Infrastructure and Practices
- 1. Immutable Infrastructure
- 2. DevOps Practices and Culture
- 3. Infrastructure as Code (IaC)
- Architecture Concepts
- 1. Serverless and FaaS
- 2. Microservices Architecture
- 3. Autoscaling (HPA, VPA, Cluster Autoscaler)
- 4. Elasticity and Resilience
|
>> Certification KCNA Questions <<
100% Pass 2026 Linux Foundation KCNA: Perfect Certification Kubernetes and Cloud Native Associate Questions
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Linux Foundation Kubernetes and Cloud Native Associate Sample Questions (Q178-Q183):
NEW QUESTION # 178
A Kubernetes deployment must run only on nodes with specific hardware capabilities. What configuration should be used to ensure that Pods are scheduled only on those nodes?
- A. Use Pod affinity rules in the deployment configuration.
- B. Set a PodDisruptionBudget for the deployment.
- C. Use a nodeSelector in the deployment specification.
- D. Configure resource limits and requests for the Pods.
Answer: C
Explanation:
A nodeSelector allows Pods to be scheduled only on nodes that have specific labels, which can represent required hardware capabilities. By labeling nodes accordingly and matching those labels in the deployment, Kubernetes ensures Pods run only on the intended nodes.
NEW QUESTION # 179
In Kubernetes, what is the primary function of a RoleBinding?
- A. To assign the permissions of a Roleto a user, group, or service account within a namespace.
- B. To create and define a new Roleobject that contains a specific set of permissions.
- C. To provide a user or group with permissions across all resources at the cluster level.
- D. To enforce namespace network rules by binding policies to Pods running in the namespace.
Answer: A
Explanation:
A RoleBinding grants the permissions defined in a Role to a specific user, group, or service account within a namespace, enabling access control scoped to that namespace.
NEW QUESTION # 180
You are deploying a new microservice on Kubernetes. The microservice needs access to a shared configuration file stored in a ConfigMap. How would you access the configuration file from within your microservice container?
- A. Use a Kubernetes Secret to store the configuration file securely.
- B. Mount the ConfigMap as a volume into the container.
- C. Define an environment variable within the pod spec and map it to the ConfigMap data.
- D. Access the ConfigMap data directly using the Kubernetes API.
- E. Use the *kubectl get' command to retrieve the configuration file from the ConfigMap.
Answer: B,C
Explanation:
Both options A and C are valid ways to access configuration data from a ConfigMap within a container Option A (Mount as a volume): You can mount the ConfigMap as a volume into the container, allowing the microservice to access the configuration file directly from the mounted directory This is useful if the configuration file is large or has complex structure. Option C (Environment variable): You can define an environment variable in the pod spec and map it to the ConfigMap data This is simpler for smaller configuration values and can be easily accessed within the microservice code- The other options are not suitable for accessing ConfigMap data within a container.
NEW QUESTION # 181
How do you perform a command in a running container of a Pod?
- A. kubectl exec <pod> -- <command>
- B. docker exec <pod> <command>
- C. kubectl run <pod> -- <command>
- D. kubectl attach <pod> -i <command>
Answer: A
Explanation:
In Kubernetes, the standard way to execute a command inside a running container is kubectl exec, which is why A is correct. kubectl exec calls the Kubernetes API (API server), which then coordinates with the kubelet on the target node to run the requested command inside the container using the container runtime's exec mechanism. The -- separator is important: it tells kubectl that everything after -- is the command to run in the container rather than flags for kubectl itself.
This is fundamentally different from docker exec. In Kubernetes, you don't normally target containers through Docker/CRI tools directly because Kubernetes abstracts the runtime behind CRI. Also, "Docker" might not even be installed on nodes in modern clusters (containerd/CRI-O are common). So option B is not the Kubernetes-native approach and often won't work.
kubectl run (option C) is for creating a new Pod (or generating workload resources), not for executing a command in an existing container. kubectl attach (option D) attaches your terminal to a running container's process streams (stdin/stdout/stderr), which is useful for interactive sessions, but it does not execute an arbitrary new command like exec does.
In real usage, you often specify the container when a Pod has multiple containers: kubectl exec -it <pod> -c
<container> -- /bin/sh. This is common for debugging, verifying config files mounted from ConfigMaps
/Secrets, testing DNS resolution, or checking network connectivity from within the Pod network namespace.
Because exec uses the API and kubelet, it respects Kubernetes access control (RBAC) and audit logging- another reason it's the correct operational method.
=========
NEW QUESTION # 182
What do you call the pattern where you add a second container to the pod to collect logs infor-mation?
- A. Node level logging
- B. Sidecar container logging
- C. Cluster level logging
- D. Application level logging
Answer: B
Explanation:
https://kubernetes.io/docs/concepts/cluster-administration/logging/

NEW QUESTION # 183
......
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