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Linux Foundation KCNA Exam Syllabus Topics:

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

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Linux Foundation Kubernetes and Cloud Native Associate Sample Questions (Q268-Q273):

NEW QUESTION # 268
What is the core metric type in Prometheus used to represent a single numerical value that can go up and down?

Answer: A

Explanation:
In Prometheus, a Gauge represents a single numerical value that can increase and decrease over time, which makes D the correct answer. Gauges are used for values like current memory usage, number of in-flight requests, queue depth, temperature, or CPU usage-anything that can move up and down.
This contrasts with a Counter, which is strictly monotonically increasing (it only goes up, except for resets when a process restarts). Counters are ideal for cumulative totals like total HTTP requests served, total errors, or bytes transmitted. Histograms and Summaries are used to capture distributions (often latency distributions), providing bucketed counts (histogram) or quantile approximations (summary), and are not the "single value that goes up and down" primitive the question asks for.
In Kubernetes observability, metrics are a primary signal for understanding system health and performance. Prometheus is widely used to scrape metrics from Kubernetes components (kubelet, API server, controller-manager), cluster add-ons, and applications. Gauges are common for resource utilization metrics and for instantaneous states, such as container_memory_working_set_bytes or go_goroutines.
When you build alerting and dashboards, selecting the right metric type matters. For example, if you want to alert on the current memory usage, a gauge is appropriate. If you want to compute request rates, you typically use counters with Prometheus functions like rate() to derive per-second rates. Histograms and summaries are used when you need latency percentiles or distribution analysis.
So, for "a single numerical value that can go up and down," the correct Prometheus metric type is Gauge (D).


NEW QUESTION # 269
What is the main difference between Argo vs. Flux CD?

Answer: D

Explanation:
AgroCD: https://argo-cd.readthedocs.io/en/stable/developer-guide/ci/#can-i-retrigger-the-checks-without-pushing-a-new-commit
FluxCD: https://fluxcd.io/


NEW QUESTION # 270
You are working on a cloud-native application that needs to interact with a database service. The application requires high availability and fault tolerance. Which open standard would you use to ensure reliable database connections and handle failures gracefully?

Answer: C

Explanation:
A Service Mesh is a crucial element in achieving high availability and fault tolerance for database connections. It provides features like load balancing, circuit breakers, and retries, which are essential for handling failures and ensuring the application remains operational even if a database instance becomes unavailable.


NEW QUESTION # 271
How does dynamic storage provisioning work?

Answer: B


NEW QUESTION # 272
Explain the concept of "service discovery" in Prometheus and how it integrates with Kubernetes.

Answer: C

Explanation:
Service discovery in Prometheus enables it to locate and collect metrics from dynamic Kubernetes environments. It automatically discovers and configures scraping targets based on Kubernetes service definitions. This simplifies the monitoring process and ensures that Prometheus stays up-to-date with changes in the Kubernetes cluster.


NEW QUESTION # 273
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