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[General] Pass Guaranteed 2026 KCNA: Kubernetes and Cloud Native Associate Pass-Sure Relia

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【General】 Pass Guaranteed 2026 KCNA: Kubernetes and Cloud Native Associate Pass-Sure Relia

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Candidates who pass the Linux Foundation KCNA Exam demonstrate their proficiency in cloud-native technologies and their ability to work effectively with Kubernetes-based applications. Kubernetes and Cloud Native Associate certification provides credibility to one's resume and proves that the individual has the necessary skills to design, deploy and manage cloud-native applications. In addition, it opens up new career opportunities and helps individuals advance their careers in the cloud-native space.
Linux Foundation KCNA (Kubernetes and Cloud Native Associate) Exam is a certification program developed by the Linux Foundation that is designed to test an individual's knowledge and skills in managing and deploying cloud-native applications and infrastructure using Kubernetes. Kubernetes and Cloud Native Associate certification is an entry-level program that is intended for IT professionals who are new to Kubernetes and cloud-native technologies.
Linux Foundation Kubernetes and Cloud Native Associate Sample Questions (Q46-Q51):NEW QUESTION # 46
You're building a new cloud-native application using gRPC for communication between microservices. You want to use Jaeger for distributed tracing to track the flow of requests through your microservices. What should you do to integrate Jaeger with your gRPC application?
  • A. Configure Istio to automatically inject tracing into gRPC calls and send the data to Jaeger.
  • B. Configure a custom gRPC interceptor to intercept gRPC calls and emit tracing data to Jaeger.
  • C. Use Jaeger's built-in support for gRPC tracing, which automatically intercepts gRPC calls and sends data to Jaeger.
  • D. Modify your gRPC client and server code to explicitly log trace information using Jaeger's logging libraries.
  • E. Use a dedicated tracing proxy that sits between your gRPC services and intercepts traffic to send tracing data to Jaeger.
Answer: A,C
Explanation:
The correct answers are B and D . Jaeger provides built-in support for tracing gRPC applications, and Istio can also be used to automatically inject tracing into gRPC calls. B: Use Jaeger's built-in support for gRPC tracing, which automatically intercepts gRPC calls and sends data to Jaeger. Jaeger offers libraries and integration points for popular frameworks like gRPC. Using Jaeger's gRPC tracing capabilities, you can automatically instrument your gRPC applications and capture tracing data without needing to manually modify your code extensively. D: Configure Istio to automatically inject tracing into gRPC calls and send the data to Jaeger. Istio, a service mesh, offers automatic tracing capabilities for gRPC applications. By configuring Istio, you can enable tracing for gRPC calls and send tracing data to Jae er streamlinin the tracin rocess and reducin code modifications within our services. The other options are incorrect or less efficient: A: Configure a custom gRPC interceptor to intercept gRPC calls and emit tracing data to Jaeger. While you can create a custom interceptor, Jaeger's built-in support and Istio's automatic tracing mechanisms are more convenient and often preferred for gRPC tracing. C: Modify your gRPC client and server code to explicitly log trace information using Jaeger's logging libraries. Manual code modifications are less efficient compared to using Jaeger's built-in support or Istio's automatic tracing. E: Use a dedicated tracing proxy that sits between your gRPC services and intercepts traffic to send tracing data to Jaeger. While a tracing proxy could work, it adds an extra layer of complexity and potential performance overhead compared to built-in support or Istio's integration.

NEW QUESTION # 47
Which of the following is a definition of Hybrid Cloud?
  • A. A combination of services running in public and private data centers, excluding serverless functions.
  • B. A combination of services running in public and private data centers, only including data centers from the same cloud provider.
  • C. A cloud native architecture that uses services running in public clouds, excluding data centers in different availability zones.
  • D. A cloud native architecture that uses services running in different public and private clouds, including on-premises data centers.
Answer: D
Explanation:
A hybrid cloud architecture combines public cloud and private/on-premises environments, often spanning multiple infrastructure domains while maintaining some level of portability, connectivity, and unified operations. Option C captures the commonly accepted definition: services run across public and private clouds, including on-premises data centers, so C is correct.
Hybrid cloud is not limited to a single cloud provider (which is why A is too restrictive). Many organizations adopt hybrid cloud to meet regulatory requirements, data residency constraints, latency needs, or to preserve existing investments while still using public cloud elasticity. In Kubernetes terms, hybrid strategies often include running clusters both on-prem and in one or more public clouds, then standardizing deployment through Kubernetes APIs, GitOps, and consistent security/observability practices.
Option B is incorrect because excluding data centers in different availability zones is not a defining property; in fact, hybrid deployments commonly use multiple zones/regions for resilience. Option D is a distraction: serverless inclusion or exclusion does not define hybrid cloud. Hybrid is about the combination of infrastructure environments, not a specific compute model.
A practical cloud-native view is that hybrid architectures introduce challenges around identity, networking, policy enforcement, and consistent observability across environments. Kubernetes helps because it provides a consistent control plane API and workload model regardless of where it runs. Tools like service meshes, federated identity, and unified monitoring can further reduce fragmentation.
So, the most accurate definition in the given choices is C: hybrid cloud combines public and private clouds, including on-premises infrastructure, to run services in a coordinated architecture.

NEW QUESTION # 48
Consider the following Kubernetes YAML definition for a Deployment:

How many Pods will be running when this Deployment is created?
  • A. 0
  • B. 1
  • C. 2
  • D. 3
  • E. 4
Answer: B
Explanation:
The 'replicas: 3' field in the Deployment specification indicates that Kubernetes should create and manage three Pods for this Deployment. The Deployment ensures that there are always three running Pods matching the specified labels.

NEW QUESTION # 49
What is the difference between a Deployment and a ReplicaSet?
  • A. With a Deployment, you can't control the number of pod replicas.
  • B. A ReplicaSet does not guarantee a stable set of replica pods running.
  • C. A Deployment is basically the same as a ReplicaSet with annotations.
  • D. A Deployment is a higher-level concept that manages ReplicaSets.
Answer: D
Explanation:
A Deployment is a higher-level controller that manages ReplicaSets and provides rollout/rollback behavior, so D is correct. A ReplicaSet's primary job is to ensure that a specified number of Pod replicas are running at any time, based on a label selector and Pod template. It's a fundamental "keep N Pods alive" controller.
Deployments build on that by managing the lifecycle of ReplicaSets over time. When you update a Deployment (for example, changing the container image tag or environment variables), Kubernetes creates a new ReplicaSet for the new Pod template and gradually shifts replicas from the old ReplicaSet to the new one according to the rollout strategy (RollingUpdate by default). Deployments also retain revision history, making it possible to roll back to a previous ReplicaSet if a rollout fails.
Why the other options are incorrect:
A is false: Deployments absolutely control the number of replicas via spec.replicas and can also be controlled by HPA.
B is false: ReplicaSets do guarantee that a stable number of replicas is running (that is their core purpose).
C is false: a Deployment is not "a ReplicaSet with annotations." It is a distinct API resource with additional controller logic for declarative updates, rollouts, and revision tracking.
Operationally, most teams create Deployments rather than ReplicaSets directly because Deployments are safer and more feature-complete for application delivery. ReplicaSets still appear in real clusters because Deployments create them automatically; you'll commonly see multiple ReplicaSets during rollout transitions. Understanding the hierarchy is crucial for troubleshooting: if Pods aren't behaving as expected, you often trace from Deployment → ReplicaSet → Pod, checking selectors, events, and rollout status.
So the key difference is: ReplicaSet maintains replica count; Deployment manages ReplicaSets and orchestrates updates. Therefore, D is the verified answer.

NEW QUESTION # 50
Which resource do you use to attach a volume in a Pod?
  • A. StorageVolume
  • B. PersistentVolumeClaim
  • C. StorageClass
  • D. PersistentVolume
Answer: B
Explanation:
In Kubernetes, Pods typically attach persistent storage by referencing a PersistentVolumeClaim (PVC), making D correct. A PVC is a user's request for storage with specific requirements (size, access mode, storage class). Kubernetes then binds the PVC to a matching PersistentVolume (PV) (either pre-provisioned statically or created dynamically via a StorageClass and CSI provisioner). The Pod does not directly attach a PV; it references the PVC, and Kubernetes handles the binding and mounting.
This design separates responsibilities: administrators (or CSI drivers) manage PV provisioning and backend storage details, while developers consume storage via PVCs. In a Pod spec, you define a volume of type persistentVolumeClaim and set claimName: <pvc-name>, then mount that volume into containers at a path. The kubelet coordinates with the CSI driver (or in-tree plugin depending on environment) to attach/mount the underlying storage to the node and then into the Pod.
Option B (PersistentVolume) is not directly referenced by Pods; PVs are cluster resources that represent actual storage. Pods don't "pick" PVs; claims do. Option C (StorageClass) defines provisioning parameters (e.g., disk type, replication, binding mode) but is not what a Pod references to mount a volume. Option A is not a Kubernetes resource type.
Operationally, using PVCs enables dynamic provisioning and portability: the same Pod spec can be deployed across clusters where the StorageClass name maps to appropriate backend storage. It also supports lifecycle controls like reclaim policies (Delete/Retain) and snapshot/restore workflows depending on CSI capabilities.
So the Kubernetes resource you use in a Pod to attach a persistent volume is PersistentVolumeClaim, option D.

NEW QUESTION # 51
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