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Linux Foundation KCNA Exam Syllabus Topics:

SectionWeightObjectives
Cloud Native Application Delivery8%- Deployment Strategies
  • 1. Rolling updates, canary releases
    • 2. Application lifecycle management
      - Delivery Models
      • 1. CI/CD pipelines
        • 2. GitOps principles
          Kubernetes Fundamentals46%- Scheduling and Administration
          • 1. Basic kubectl operations
            • 2. Scheduler concepts
              - Kubernetes Core Concepts
              • 1. Cluster architecture and components
                • 2. Pods, Controllers, Services
                  • 3. API resources and objects
                    - Containerization Basics
                    • 1. Containers vs VMs
                      • 2. Images and registries
                        Cloud Native Observability8%- Logging & Tracing
                        • 1. Centralized logging
                          • 2. Distributed tracing basics
                            - Monitoring & Metrics
                            • 1. Core observability signals
                              • 2. Tools and standards
                                Container Orchestration22%- Networking & Storage
                                • 1. Cluster networking model
                                  • 2. Persistent storage concepts
                                    - Security & Troubleshooting
                                    • 1. Security fundamentals
                                      • 2. Basic troubleshooting methods
                                        - Orchestration Principles
                                        • 1. Why orchestration matters
                                          • 2. Runtime environments
                                            Cloud Native Architecture16%- Ecosystem & Landscape
                                            • 1. CNCF projects overview
                                              • 2. Serverless, service mesh concepts
                                                - Cloud Native Principles
                                                • 1. Microservices design
                                                  • 2. 12-factor applications

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                                                    Linux Foundation Kubernetes and Cloud Native Associate Sample Questions (Q305-Q310):

                                                    NEW QUESTION # 305
                                                    What are the two major components of service mesh?

                                                    Answer: C

                                                    Explanation:
                                                    https://istio.io/latest/about/service-mesh/


                                                    NEW QUESTION # 306
                                                    What is Serverless computing?

                                                    Answer: C


                                                    NEW QUESTION # 307
                                                    You have a Kubernetes cluster with three worker nodes. Nodel has 8 CPU cores, Node2 has 4 CPU cores, and Node3 has 2 CPU cores. You deploy a pod with a resource request of 2 CPU cores. What is the likely order in which Kubernetes will attempt to schedule this pod on the available nodes?

                                                    Answer: C

                                                    Explanation:
                                                    Kubernetes typically attempts to schedule pods on nodes that have the most available resources to meet the pod's requests. In this scenario, Nodel has the highest CPU capacity, followed by Node2, and then Node3. The scheduler prioritizes nodes with more available resources to ensure optimal utilization and minimize potential resource contention among pods.


                                                    NEW QUESTION # 308
                                                    What is a sidecar container?

                                                    Answer: D

                                                    Explanation:
                                                    A sidecar container is an additional container that runs alongside the main application container within the same Pod, sharing network and storage context. That matches option C, so C is correct. The sidecar pattern is used to add supporting capabilities to an application without modifying the application code. Because both containers are in the same Pod, the sidecar can communicate with the main container over localhost and share volumes for files, sockets, or logs.
                                                    Common sidecar examples include: log forwarders that tail application logs and ship them to a logging system, proxies (service mesh sidecars like Envoy) that handle mTLS and routing policy, config reloaders that watch ConfigMaps and signal the main process, and local caching agents. Sidecars are especially powerful in cloud-native systems because they standardize cross-cutting concerns-security, observability, traffic policy-across many workloads.
                                                    Options A and D incorrectly describe "a Pod running next to ..." which is not how sidecars work; sidecars are containers, not separate Pods. Running separate Pods "next to" each other in a namespace does not give the same shared network namespace and tightly coupled lifecycle. Option B is also incorrect for the same reason: a sidecar is not a separate Pod; it is a container in the same Pod.
                                                    Operationally, sidecars share the Pod lifecycle: they are scheduled together, scaled together, and generally terminated together. This is both a benefit (co-location guarantees) and a responsibility (resource requests/limits should include the sidecar's needs, and failure modes should be understood). Kubernetes is increasingly formalizing sidecar behavior (e.g., sidecar containers with ordered startup semantics), but the core definition remains: a helper container in the same Pod.


                                                    NEW QUESTION # 309
                                                    Which of the following resources helps in managing a stateless application workload on a Kubernetes cluster?

                                                    Answer: A

                                                    Explanation:
                                                    A Deployment manages stateless application workloads by ensuring the desired number of pod replicas are running and handling updates and rollbacks.


                                                    NEW QUESTION # 310
                                                    ......

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