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[Hardware] Top Reliable KCNA Exam Sims | Professional Linux Foundation KCNA: Kubernetes and

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【Hardware】 Top Reliable KCNA Exam Sims | Professional Linux Foundation KCNA: Kubernetes and

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Linux Foundation KCNA (Kubernetes and Cloud Native Associate) Exam is a certification program designed to validate the skills and knowledge of professionals who work with cloud-native technologies such as Kubernetes. The program is offered by the Linux Foundation, a non-profit organization that is dedicated to promoting the growth of Linux and open-source software.
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Linux Foundation KCNA (Kubernetes and Cloud Native Associate) Exam is a certification program that is designed to provide individuals with the skills and knowledge required to work with Kubernetes and cloud-native technologies. Kubernetes and Cloud Native Associate certification is ideal for individuals who are interested in pursuing a career in the field of cloud computing or for those who want to enhance their skills in this area. The program covers various areas such as containerization, orchestration, networking, security, and storage.
Linux Foundation Kubernetes and Cloud Native Associate Sample Questions (Q144-Q149):NEW QUESTION # 144
Why is Cloud-Native Architecture important?
  • A. Cloud Native Architecture revolves around containers, microservices and pipelines.
  • B. Cloud Native Architecture removes constraints to rapid innovation.
  • C. Cloud Native Architecture is modern for application deployment and pipelines.
  • D. Cloud Native Architecture is a bleeding edge technology and service.
Answer: B
Explanation:
Cloud-native architecture is important because it enables organizations to build and run software in a way that supports rapid innovation while maintaining reliability, scalability, and efficient operations. Option B best captures this: cloud native removes constraints to rapid innovation, so B is correct.
In traditional environments, innovation is slowed by heavyweight release processes, tightly coupled systems, manual operations, and limited elasticity. Cloud-native approaches-containers, declarative APIs, automation, and microservices-friendly patterns-reduce those constraints. Kubernetes exemplifies this by offering a consistent deployment model, self-healing, automated rollouts, scaling primitives, and a large ecosystem of delivery and observability tools. This makes it easier to ship changes more frequently and safely: teams can iterate quickly, roll back confidently, and standardize operations across environments.
Option A is partly descriptive (containers/microservices/pipelines are common in cloud native), but it doesn't explain why it matters; it lists ingredients rather than the benefit. Option C is vague ("modern") and again doesn't capture the core value proposition. Option D is incorrect because cloud native is not primarily about being "bleeding edge"-it's about proven practices that improve time-to-market and operational stability.
A good way to interpret "removes constraints" is: cloud native shifts the bottleneck away from infrastructure friction. With automation (IaC/GitOps), standardized runtime packaging (containers), and platform capabilities (Kubernetes controllers), teams spend less time on repetitive manual work and more time delivering features. Combined with observability and policy automation, this results in faster delivery with better reliability-exactly the reason cloud-native architecture is emphasized across the Kubernetes ecosystem.

NEW QUESTION # 145
What are the characteristics for building every cloud-native application?
  • A. Resiliency, Containerd, Observability, Agility
  • B. Kubernetes, Operability, Observability, Availability
  • C. Resiliency, Agility, Operability, Observability
  • D. Resiliency, Operability, Observability, Availability
Answer: C
Explanation:
Cloud-native applications are typically designed to thrive in dynamic, distributed environments where infrastructure is elastic and failures are expected. The best set of characteristics listed is Resiliency, Agility, Operability, Observability, making D correct.
Resiliency means the application and its supporting platform can tolerate failures and continue providing service. In Kubernetes terms, resiliency is supported through self-healing controllers, replica management, health probes, and safe rollout mechanisms, but the application must also be designed to handle transient failures, retries, and graceful degradation.
Agility reflects the ability to deliver changes quickly and safely. Cloud-native systems emphasize automation, CI/CD, declarative configuration, and small, frequent releases-often enabled by Kubernetes primitives like Deployments and rollout strategies. Agility is about reducing the friction to ship improvements while maintaining reliability.
Operability is how manageable the system is in production: clear configuration, predictable deployments, safe scaling, and automation-friendly operations. Kubernetes encourages operability through consistent APIs, controllers, and standardized patterns for configuration and lifecycle.
Observability means you can understand what's happening inside the system using telemetry-metrics, logs, and traces-so you can troubleshoot issues, measure SLOs, and improve performance. Kubernetes provides many integration points for observability, but cloud-native apps must also emit meaningful signals.
Options B and C include items that are not "characteristics" (containerd is a runtime; Kubernetes is a platform). Option A includes "availability," which is important, but the canonical cloud-native framing in this question emphasizes the four qualities in D as the foundational build characteristics.

NEW QUESTION # 146
What is the reference implementation of the OCI runtime specification?
  • A. CRI-O
  • B. lxc
  • C. Docker
  • D. runc
Answer: D
Explanation:
The verified correct answer is C (runc). The Open Container Initiative (OCI) defines standards for container image format and runtime behavior. The OCI runtime specification describes how to run a container (process execution, namespaces, cgroups, filesystem mounts, capabilities, etc.). runc is widely recognized as the reference implementation of that runtime spec and is used underneath many higher-level container runtimes.
In common container stacks, Kubernetes nodes typically run a CRI-compliant runtime such as containerd or CRI-O. Those runtimes handle image management, container lifecycle coordination, and CRI integration, but they usually invoke an OCI runtime to actually create and start containers. In many deployments, that OCI runtime is runc (or a compatible alternative). This layering helps keep responsibilities separated: CRI runtime manages orchestration-facing operations; OCI runtime performs the low-level container creation according to the standardized spec.
Option A (lxc) is an older Linux containers technology and tooling ecosystem, but it is not the OCI runtime reference implementation. Option B (CRI-O) is a Kubernetes-focused container runtime that implements CRI; it uses OCI runtimes (often runc) underneath, so it's not the reference implementation itself. Option D (Docker) is a broader platform/tooling suite; while Docker historically used runc under the hood and helped popularize containers, the OCI reference runtime implementation is runc, not Docker.
Understanding this matters in container orchestration contexts because it clarifies what Kubernetes depends on: Kubernetes relies on CRI for runtime integration, and runtimes rely on OCI standards for interoperability. OCI standards ensure that images and runtime behavior are portable across tools and vendors, and runc is the canonical implementation that demonstrates those standards in practice.
Therefore, the correct answer is C: runc.

NEW QUESTION # 147
Which mechanism can be used to automatically adjust the amount of resources for an application?
  • A. Kubernetes Event-driven Autoscaling (KEDA)
  • B. Cluster Autoscaler
  • C. Horizontal Pod Autoscaler (HPA)
  • D. Vertical Pod Autoscaler (VPA)
Answer: C
Explanation:
The verified answer in the PDF is A (HPA), and that aligns with the common Kubernetes meaning of "adjust resources for an application" by scaling replicas. The Horizontal Pod Autoscaler automatically changes the number of Pod replicas for a workload (typically a Deployment) based on observed metrics such as CPU utilization, memory (in some configurations), or custom/external metrics. By increasing replicas under load, the application gains more total CPU/memory capacity available across Pods; by decreasing replicas when load drops, it reduces resource consumption and cost.
It's important to distinguish what each mechanism adjusts:
HPA adjusts replica count (horizontal scaling).
VPA adjusts Pod resource requests/limits (vertical scaling), which is literally "amount of CPU/memory per pod," but it often requires restarts to apply changes depending on mode.
Cluster Autoscaler adjusts the number of nodes in the cluster, not application replicas.
KEDA is event-driven autoscaling that often drives HPA behavior using external event sources (queues, streams), but it's not the primary built-in mechanism referenced in many foundational Kubernetes questions.
Given the wording and the provided answer key, the intended interpretation is: "automatically adjust the resources available to the application" by scaling out/in the number of replicas. That's exactly HPA's role. For example, if CPU utilization exceeds a target (say 60%), HPA computes a higher desired replica count and updates the workload. The Deployment then creates more Pods, distributing load and increasing available compute.
So, within this question set, the verified correct choice is A (Horizontal Pod Autoscaler).

NEW QUESTION # 148
Which of the following best describes the concept of 'cold starts' in serverless computing?
  • A. The time required for a serverless function to scale up to handle increased workload.
  • B. The latency introduced by network communication between a serverless function and its dependencies.
  • C. The overhead associated with invoking a serverless function from an external client.
  • D. The time taken for a serverless function to access and process data from a database.
  • E. The time it takes for a serverless function to be initialized and ready to execute after a period of inactivity.
Answer: E
Explanation:
Cold starts refer to the time it takes for a serverless function to be provisioned, loaded into memory, and ready for execution after a period of inactivity. This latency is a characteristic of serverless environments and can affect performance, especially for infrequent function invocations. Options B, C, D, and E describe other factors that can contribute to latency in serverless applications but are not the defining characteristic of cold starts

NEW QUESTION # 149
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