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Linux Foundation KCNA Exam Syllabus Topics:

Section	Weight	Objectives
Cloud Native Observability	8%	- Monitoring and Metrics 1. Dashboards and Visualization (Grafana) 2. Prometheus and Metrics Collection - Logging 1. Centralized Logging (Fluentd, Elasticsearch, Kibana) 2. Kubernetes Logging Architecture - Tracing 1. Distributed Tracing Concepts (OpenTelemetry, Jaeger)
Kubernetes Fundamentals	46%	- Kubernetes API 1. Declarative Management (Manifests/YAML) 2. API Resource Structure and Versioning - Containers 1. Container Images and Registries 2. Container Runtime Interface (CRI) 3. Basic kubectl Commands - Scheduling 1. Resource Requests and Limits 2. Node Selection and Affinity 3. Taints and Tolerations - 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)
Container Orchestration	22%	- Orchestration Fundamentals 1. Self-healing and Rolling Updates 2. Service Discovery and Load Balancing 3. Scheduling and Resource Management - Storage 1. Storage Classes and Dynamic Provisioning 2. Volumes, PersistentVolumes (PV), PersistentVolumeClaims (PVC) - Service Mesh 1. Sidecar Pattern and Traffic Management 2. Service Mesh Concepts (Istio, Linkerd) - Security 1. RBAC (Role-Based Access Control) 2. Pod Security Standards (Admission Control) 3. Network Policies - Networking 1. Kubernetes Networking Model 2. CoreDNS and Service Networking - Container Runtimes 1. Docker, containerd, CRI-O
Cloud Native Application Delivery	8%	- Deployment Strategies 1. Blue/Green, Canary, Rolling Updates - GitOps 1. Tools (Argo CD, Flux) 2. GitOps Principles and Workflow - CI/CD 1. Artifact Management and Image Registries 2. Continuous Integration and Continuous Delivery Pipelines
Cloud Native Architecture	16%	- Architecture Concepts 1. Elasticity and Resilience 2. Microservices Architecture 3. Serverless and FaaS 4. Autoscaling (HPA, VPA, Cluster Autoscaler) - 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)

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Linux Foundation Kubernetes and Cloud Native Associate Sample Questions (Q170-Q175):

NEW QUESTION # 170
How do you deploy a workload to Kubernetes without additional tools?

A. Create a Helm Chart and install it with helm.
B. Create a manifest and apply it with kubectl.
C. Create a Bash script and run it on a worker node.
D. Create a Python script and run it with kubectl.

Answer: B

Explanation:
The standard way to deploy workloads to Kubernetes using only built-in tooling is to create Kubernetes manifests (YAML/JSON definitions of API objects) and apply them with kubectl, so C is correct.
Kubernetes is a declarative system: you describe the desired state of resources (e.g., a Deployment, Service, ConfigMap, Ingress) in a manifest file, then submit that desired state to the API server. Controllers reconcile the actual cluster state to match what you declared.
A manifest typically includes mandatory fields like apiVersion, kind, and metadata, and then a spec describing desired behavior. For example, a Deployment manifest declares replicas and the Pod template (containers, images, ports, probes, resources). Applying the manifest with kubectl apply -f <file> creates or updates the resources. kubectl apply is also designed to work well with iterative changes: you update the file, re-apply, and Kubernetes performs a controlled rollout based on controller logic.
Option B (Helm) is indeed a popular deployment tool, but Helm is explicitly an "additional tool" beyond kubectl and the Kubernetes API. The question asks "without additional tools," so Helm is excluded by definition. Option A (running Bash scripts on worker nodes) bypasses Kubernetes' desired-state control and is not how Kubernetes workload deployment is intended; it also breaks portability and operational safety.
Option D is not a standard Kubernetes deployment mechanism; kubectl does not "run Python scripts" to deploy workloads (though scripts can automate kubectl, that's still not the primary mechanism).
From a cloud native delivery standpoint, manifests support GitOps, reviewable changes, and repeatable deployments across environments. The Kubernetes-native approach is: declare resources in manifests and apply them to the cluster. Therefore, C is the verified correct answer.

NEW QUESTION # 171
Which of the following options include resources cleaned by the Kubernetes garbage collection mechanism?

A. Nodes deleted by a cloud controller manager and obsolete logs from the kubelet.
B. Terminated pods, completed jobs, and objects without owner references.
C. Unused container and container images, and obsolete logs from the kubelet.
D. Stale or expired CertificateSigningRequests (CSRs) and old deployments.

Answer: B

Explanation:
Kubernetes garbage collection cleans up terminated pods, completed jobs, and objects that no longer have owner references to free cluster resources automatically.

NEW QUESTION # 172
In a serverless computing architecture:

A. Users should make a reservation to the cloud provider based on an estimation of usage.
B. Serverless functions are incompatible with containerized functions.
C. Users of the cloud provider are charged based on the number of requests to a function.
D. Containers serving requests are running in the background in idle status.

Answer: C

NEW QUESTION # 173
What are the two steps performed by the kube-scheduler to select a node to schedule a pod?

A. Filtering and selecting
B. Scoring and creating
C. Grouping and placing
D. Filtering and scoring

Answer: D

Explanation:
The kube-scheduler selects a node in two main phases: filtering and scoring, so C is correct. First, filtering identifies which nodes are feasible for the Pod by applying hard constraints. These include resource availability (CPU/memory requests), node taints/tolerations, node selectors and required affinities, topology constraints, and other scheduling requirements. Nodes that cannot satisfy the Pod's requirements are removed from consideration.
Second, scoring ranks the remaining feasible nodes using priority functions to choose the "best" placement.
Scoring can consider factors like spreading Pods across nodes/zones, packing efficiency, affinity preferences, and other policies configured in the scheduler. The node with the highest score is selected (with tie-breaking), and the scheduler binds the Pod by setting spec.nodeName.
Option B ("filtering and selecting") is close but misses the explicit scoring step that is central to scheduler design. The scheduler does "select" a node, but the canonical two-step wording in Kubernetes scheduling is filtering then scoring. Options A and D are not how scheduler internals are described.
Operationally, understanding filtering vs scoring helps troubleshoot scheduling failures. If a Pod can't be scheduled, it failed in filtering-kubectl describe pod often shows "0/... nodes are available" reasons (insufficient CPU, taints, affinity mismatch). If it schedules but lands in unexpected places, it's often about scoring preferences (affinity weights, topology spread preferences, default scheduler profiles).
So the verified correct answer is C: kube-scheduler uses Filtering and Scoring.
=========

NEW QUESTION # 174
Which command correctly uses kubectl exec to inspect environment variables inside a running container?

A. Run kubectl exec <pod-name> -- env
B. Run kubectl exec <pod-name> -- show variables
C. Run kubectl exec <pod-name> -- get env
D. Run kubectl exec <pod-name> -- list config

Answer: A

Explanation:
The kubectl exec command runs a command inside a container, and env is the standard Linux command to display environment variables within that running container.

NEW QUESTION # 175
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