Kubernetes and Cloud Native KCNA Exam Dumps

The practice of bringing financial accountability to cloud spending—where costs are variable and usage-based—is called FinOps, so D is correct. FinOps (Financial Operations) is an operating model and culture that helps organizations manage cloud costs by connecting engineering, finance, and business teams. Because cloud resources can be provisioned quickly and billed dynamically, traditional budgeting approaches often fail to keep pace. FinOps addresses this by introducing shared visibility, governance, and optimization processes that enable teams to make cost-aware decisions while still moving fast.

In Kubernetes and cloud-native architectures, variable spend shows up in many ways: autoscaling node pools, over-provisioned resource requests, idle clusters, persistent volumes, load balancers, egress traffic, managed services, and observability tooling. FinOps practices encourage tagging/labeling for cost attribution, defining cost KPIs, enforcing budget guardrails, and continuously optimizing usage (right-sizing resources, scaling policies, turning off unused environments, and selecting cost-effective architectures).

Why the other options are incorrect: FaaS (Function as a Service) is a compute model (serverless), not a financial accountability practice. DevOps is a cultural and technical practice focused on collaboration and delivery speed, not specifically cloud cost accountability (though it can complement FinOps). CloudCost is not a widely recognized standard term in the way FinOps is.

In practice, FinOps for Kubernetes often involves improving resource efficiency: aligning requests/limits with real usage, using HPA/VPA appropriately, selecting instance types that match workload profiles, managing cluster autoscaler settings, and allocating shared platform costs to teams via labels/namespaces. It also includes forecasting and anomaly detection, because cloud-native spend can spike quickly due to misconfigurations (e.g., runaway autoscaling or excessive log ingestion).

So, the correct term for financial accountability in cloud variable spend is FinOps (D).

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In Kubernetes, applications that need to interact with the Kubernetes API should never use a developer’s personal credentials. Instead, Kubernetes provides a built-in, secure, and auditable mechanism for workload authentication and authorization using ServiceAccounts. Creating a dedicated ServiceAccount and binding it to the Pod is the correct and recommended approach, making option A the correct answer.

A ServiceAccount represents an identity for processes running inside Pods. When a Pod is configured to use a specific ServiceAccount, Kubernetes automatically injects a short-lived authentication token into the Pod. This token is securely mounted and can be used by the application to authenticate to the Kubernetes API server. Access to API resources is then controlled using RBAC (Role-Based Access Control) by binding roles or cluster roles to the ServiceAccount, ensuring the application has only the permissions it needs—following the principle of least privilege.

Option B is incorrect because manually generating certificates for application access is not the standard or recommended method for in-cluster authentication. Kubernetes manages ServiceAccount tokens automatically and rotates them as needed, providing a simpler and more secure solution. Option C is incorrect because using a developer’s kubeconfig file inside an application introduces serious security risks and violates best practices by coupling workloads to personal credentials. Option D is also incorrect because relying on the default ServiceAccount is discouraged; it often has no permissions or, in some cases, broader permissions than intended. Creating a dedicated ServiceAccount provides clearer security boundaries and auditability.

Using ServiceAccounts integrates cleanly with Kubernetes’ authentication and authorization model and is explicitly designed for applications and controllers running inside the cluster. This approach ensures secure API access, centralized permission management, and operational consistency across environments.

Therefore, the correct and verified answer is Option A: Create a ServiceAccount and bind it to the Pod for API access.

CNCF stands for the Cloud Native Computing Foundation, making B correct. CNCF is the foundation that hosts and sustains many cloud-native open source projects, including Kubernetes, and provides governance, neutral stewardship, and community infrastructure to help projects grow and remain vendor-neutral.

CNCF’s scope includes not only Kubernetes but also a broad ecosystem of projects across observability, networking, service meshes, runtime security, CI/CD, and application delivery. The foundation defines processes for project incubation and graduation, promotes best practices, organizes community events, and supports interoperability and adoption through reference architectures and education.

In the Kubernetes context, CNCF’s role matters because Kubernetes is a massive multi-vendor project. Neutral governance reduces the risk that any single company can unilaterally control direction. This fosters broad contribution and adoption across cloud providers and enterprises. CNCF also supports the broader “cloud native” definition, often associated with containerization, microservices, declarative APIs, automation, and resilience principles.

The incorrect options are close-sounding but not accurate expansions. “Cloud Native Community Foundation” and the “Cloud Neutral …” variants are not the recognized meaning. The correct official name is Cloud Native Computing Foundation.

So, the verified answer is B, and understanding CNCF helps connect Kubernetes to its broader ecosystem of standardized, interoperable cloud-native tooling.

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The correct answer is C: Kubernetes scalability guidance commonly cites support up to 5000 nodes and recommends no more than 110 Pods per node. The “110 Pods per node” recommendation is a practical limit based on kubelet, networking, and IP addressing constraints, as well as performance characteristics for scheduling, service routing, and node-level resource management. It is also historically aligned with common CNI/IPAM defaults where node Pod CIDRs are sized for ~110 usable Pod IPs.

Why the other options are incorrect: A and D reference “containers per node,” which is not the standard sizing guidance (Kubernetes typically discusses Pods per node). B’s “500 Pods per node” is far above typical recommended limits for many environments and would stress IPAM, kubelet, and node resources significantly.

In large clusters, several considerations matter beyond the headline limits: API server and etcd performance, watch/list traffic, controller reconciliation load, CoreDNS scaling, and metrics/observability overhead. You must also plan for IP addressing (cluster CIDR sizing), node sizes (CPU/memory), and autoscaling behavior. On each node, kubelet and the container runtime must handle churn (starts/stops), logging, and volume operations. Networking implementations (kube-proxy, eBPF dataplanes) also have scaling characteristics.

Kubernetes provides patterns to keep systems stable at scale: request/limit discipline, Pod disruption budgets, topology spread constraints, namespaces and quotas, and careful observability sampling. But the exam-style fact this question targets is the published scalability figure and per-node Pod recommendation.

Therefore, the verified true statement among the options is C.

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