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| Certification Vendor: | Linux Foundation / CNCF |
|---|---|
| Exam Name: | Kubernetes and Cloud Native Associate (KCNA) Exam |
| Exam Number: | KCNA |
| Real Exam Qty: | 60 |
| Passing Score: | 75% |
| Exam Format: | Online proctored, Multiple choice |
| Exam Price: | $250 USD |
| Certificate Validity Period: | 2 years |
| Related Certifications: | CKS (Certified Kubernetes Security Specialist) KCSA (Kubernetes and Cloud Native Security Associate) CKA (Certified Kubernetes Administrator) CKAD (Certified Kubernetes Application Developer) |
| Available Languages: | Japanese, English |
| Exam Duration: | 90 minutes |
| Recommended Training: | LFS250: Kubernetes and Cloud Native Essentials |
| Exam Registration: | Official Registration |
| Sample Questions: | Linux Foundation KCNA Sample Questions |
| Exam Way: | Online, remotely proctored; available worldwide |
| Pre Condition: | No mandatory prerequisites; basic IT knowledge and familiarity with command-line tools recommended |
| Official Syllabus URL: | https://training.linuxfoundation.org/certification/kubernetes-cloud-native-associate/ |
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The KCNA certification exam consists of 50 multiple-choice questions that cover various topics related to Kubernetes and cloud-native technologies such as containerization, Kubernetes architecture, Kubernetes deployment, and application management. KCNA Exam is designed to evaluate the candidate's understanding of these topics and their ability to apply this knowledge in real-world scenarios.
NEW QUESTION # 191
You have a Kubernetes cluster with multiple nodes. You want to ensure that your application Pods are evenly distributed across these nodes. Which Kubernetes feature should you use for this purpose?
Answer: B
Explanation:
NodeSelector allows you to specify labels that your Pods must match in order to be scheduled on a specific node. By using NodeSelector, you can distribute your Pods across different nodes based on their resources, location, or any other criteria you define.
NEW QUESTION # 192
Which Kubernetes-native deployment strategy supports zero-downtime updates of a workload?
Answer: B
Explanation:
D (RollingUpdate) is correct. In Kubernetes, the Deployment resource's default update strategy is RollingUpdate, which replaces Pods gradually rather than all at once. This supports zero-downtime updates when the workload is properly configured (sufficient replicas, correct readiness probes, and appropriate maxUnavailable / maxSurge settings). As new Pods come up and become Ready, old Pods are terminated in a controlled way, keeping the service available throughout the rollout.
RollingUpdate's "zero downtime" is achieved by maintaining capacity while transitioning between versions. For example, with multiple replicas, Kubernetes can create new Pods, wait for readiness, then scale down old Pods, ensuring traffic continues to flow to healthy instances. Readiness probes are critical: they prevent traffic from being routed to a Pod until it's actually ready to serve.
Why other options are not the Kubernetes-native "strategy" answer here:
Recreate (B) explicitly stops old Pods before starting new ones, causing downtime for most services.
Canary (A) and BlueGreen (C) are real deployment patterns, but in "Kubernetes-native deployment strategy" terms, the built-in Deployment strategies are RollingUpdate and Recreate. Canary/BlueGreen typically require additional tooling/controllers (service mesh, ingress controller features, or progressive delivery operators) to manage traffic shifting between versions.
So, for a Kubernetes-native strategy that supports zero-downtime updates, the correct and verified choice is RollingUpdate (D).
NEW QUESTION # 193
Fluentd is the only way to export logs from Kubernetes cluster or applications running in cluster
Answer: A
Explanation:
https://github.com/cncf/landscape#trail-map
NEW QUESTION # 194
Which tool is used to streamline installing and managing Kubernetes applications?
Answer: B
Explanation:
Helm is the Kubernetes package manager used to streamline installing and managing applications, so B is correct. Helm packages Kubernetes resources into charts, which contain templates, default values, and metadata. When you install a chart, Helm renders templates into concrete manifests and applies them to the cluster. Helm also tracks a "release," enabling upgrades, rollbacks, and consistent lifecycle operations across environments.
This is why Helm is widely used for complex applications that require multiple Kubernetes objects (Deployments/StatefulSets, Services, Ingresses, ConfigMaps, RBAC, CRDs). Rather than manually maintaining many YAML files per environment, teams can parameterize configuration with values and reuse the same chart across dev/stage/prod with different overrides.
Option A (apt) and option D (brew) are OS package managers (Debian/Ubuntu and macOS/Linuxbrew respectively), not Kubernetes application managers. Option C (service) is a Linux service manager command pattern and not relevant here.
In cloud-native delivery pipelines, Helm often integrates with GitOps and CI/CD: the pipeline builds an image, updates chart values (image tag/digest), and deploys via Helm or via GitOps controllers that render/apply Helm charts. Helm also supports chart repositories and versioning, making it easier to standardize deployments and manage dependencies.
So, the verified tool for streamlined Kubernetes app install/management is Helm (B).
NEW QUESTION # 195
You need to create a PersistentVolume (PV) for a database with a specific access mode and a pre-configured storage class. Which of the following YAML snippets correctly defines a PV for this purpose?





Answer: D
Explanation:
NEW QUESTION # 196
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