KCNA Exam Dumps - Kubernetes and Cloud Native Associate

If a Pod is waiting for its container images to be pulled to the node, it remains in thePendingphase, soDis correct. Kubernetes Pod “phase” is a high-level summary of where the Pod is in its lifecycle.Pendingmeans the Pod has been accepted by the cluster but one or more of its containers has not started yet. That can occur because the Pod is waiting to be scheduled, waiting on volume attachment/mount, or—very commonly—waiting for the container runtime to pull the image.

When image pulling is the blocker, kubectl describe pod usually shows events like “Pulling image …” and “Successfully pulled image …” or failures like ImagePullBackOff/ErrImagePull. Even if the node has been assigned (scheduler has set spec.nodeName), the Pod can still be Pending while kubelet and the runtime perform preparation steps.

Why the other phases don’t apply:

Succeededis for run-to-completion Pods that have finished successfully (typical for Jobs).

Failedmeans the Pod terminated and at least one container terminated in failure (and won’t be restarted, depending on restartPolicy).

Unknownis used when the node can’t be contacted and the Pod’s state can’t be reliably determined (rare in healthy clusters).

A subtle but important Kubernetes detail: status “Waiting” reasons like ImagePullBackOff are container states inside .status.containerStatuses, while the Pod phase can still be Pending. So, “waiting for images to download” maps to Pod Pending, with container waiting reasons providing the deeper diagnosis.

Therefore, the verified correct answer isD: Pending.

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ACRDis aCustomResourceDefinition, makingAcorrect. Kubernetes is built around an API-driven model: resources like Pods, Services, and Deployments are all objects served by the Kubernetes API. CRDs allow you toextend the Kubernetes APIby defining your own resource types. Once a CRD is installed, the API server can store and serve custom objects (Custom Resources) of that new type, and Kubernetes tooling (kubectl, RBAC, admission, watch mechanisms) can interact with them just like built-in resources.

CRDs are a core building block of the Kubernetes ecosystem because they enable operators and platform extensions. A typical pattern is: define a CRD that represents the desired state of some higher-level concept (for example, a database cluster, a certificate request, an application release), and then run a controller (often called an “operator”) that watches those custom resources and reconciles the cluster to match. That controller may create Deployments, StatefulSets, Services, Secrets, or cloud resources to implement the desired state encoded in the custom resource.

The incorrect answers are made-up expansions. CRDs are not related to Rust in Kubernetes terminology, and “custom restricted definition” is not the standard meaning.

So the verified meaning is:CRD = CustomResourceDefinition, used to extend Kubernetes APIs and enable Kubernetes-native automation via controllers/operators.

TheKubernetes APIis the central interface that enables communication between users, controllers, nodes, and external integrations, soCis correct. Kubernetes is fundamentally an API-driven system: all cluster state is represented as API objects, and all operations—create, update, delete, watch—flow through the API server.

End users typically interact with the Kubernetes API using tools likekubectl, client libraries, or dashboards. But those tools are clients; the shared communication “hub” is the API itself. Inside the cluster, core control plane components (controllers, scheduler) continuously watch the API for desired state and write status updates back. Worker nodes (via kubelet) also communicate with the API server to receive Pod specs, report node health, and update Pod statuses. External systems—cloud provider integrations, CI/CD pipelines, GitOps controllers, monitoring and policy engines—also integrate primarily through the Kubernetes API.

Option A (kubectl) is a CLI that talks to the Kubernetes API; it is not the underlying component that all parts use to communicate. Options B and D are cloud-provider tools and are not universal to Kubernetes clusters. Kubernetes runs across many environments, and the consistent interoperability layer is the Kubernetes API.

This API-centric architecture is what enables Kubernetes’ declarative model: you submit desired state to the API, and controllers reconcile actual state to match. It also enables extensibility: CRDs and admission webhooks expand what the API can represent and enforce. Therefore, the correct answer isC: Kubernetes API.

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In Prometheus, aGaugeis the metric type used to represent a value that canincrease and decreaseover time, soBis correct. Gauges are suited for “current state” measurements such as current memory usage, number of active sessions, queue depth, temperature, or CPU usage—anything that can move up and down as the system changes.

This contrasts with aCounter(A), which is monotonically increasing (it only goes up, except when a process restarts and the counter resets to zero). Counters are ideal for totals like total HTTP requests served, total errors, or bytes sent, and you typically use rate()/irate() in PromQL to convert counters into per-second rates.

ASummary(C) andHistogram(D) are used for distributions, commonly request latency. Histograms record observations into buckets and can produce percentiles using functions like histogram_quantile(). Summaries compute quantiles on the client side and expose them directly, along with counts and sums. Neither of these is the simplest “single value that goes up and down” type.

In Kubernetes observability, Prometheus is often used to scrape metrics from cluster components (API server, kubelet) and applications. Choosing the right metric type matters operationally: use gauges for instantaneous measurements, counters for event totals, and histograms/summaries for latency distributions. That’s why Prometheus documentation and best practices emphasize understanding metric semantics—because misusing types leads to incorrect alerts and dashboards.

So for a single numeric value that can go up and down, the correct metric type isGauge, optionB.

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The most common visualization tool used with Prometheus isGrafana, soAis correct. Prometheus includes a built-in expression browser that can graph query results, but Grafana provides a much richer dashboarding experience: reusable dashboards, variables, templating, annotations, alerting integrations, and multi-data-source support.

In Kubernetes observability stacks, Prometheus scrapes and stores time-series metrics (cluster and application metrics). Grafana queries Prometheus using PromQL and renders the results into dashboards for SREs and developers. This pairing is widespread because it cleanly separates concerns: Prometheus is the metrics store and query engine; Grafana is the UI and dashboard layer.

Option B (Graphite) is a separate metrics system with its own storage/query model; while Grafana can visualize Graphite too, the question asks what is commonly usedtogether with Prometheus, which is Grafana. Option D (GraphQL) is an API query language, not a metrics visualization tool. Option C (“Nirvana”) is not a standard Prometheus visualization tool in common Kubernetes stacks.

In practice, this combo enables operational outcomes: dashboards for error rates and latency (often derived from histograms), capacity monitoring (node CPU/memory), workload behavior (Pod restarts, HPA scaling), and SLO reporting. Grafana dashboards often serve as the shared language during incidents: teams correlate alerts with time-series patterns and quickly identify when regressions began.

Therefore, the verified correct tool commonly used with Prometheus for visualization isGrafana (A).

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Horizontal scaling means changinghow many instancesof an application are running, not changing how big each instance is. Therefore, the best description isC: adding/removing application instances of the same application to meet demand. In Kubernetes, “instances” typically correspond toPod replicasmanaged by a controller like a Deployment. When you scale horizontally, you increase or decrease the replica count, which increases or decreases total throughput and resilience by distributing load across more Pods.

Option A is aboutcluster/node scaling(adding or removing nodes), which is infrastructure scaling typically handled by a cluster autoscaler in cloud environments. Node scaling can enable more Pods to be scheduled, but it’s not the definition of horizontal application scaling itself. Option D describesvertical scaling—adding/removing CPU or memory resources to a given instance (Pod/container) by changing requests/limits or using VPA. Option B is vague and not the standard definition.

Horizontal scaling is a core cloud-native pattern because it improves availability and elasticity. If one Pod fails, other replicas continue serving traffic. In Kubernetes, scaling can be manual (kubectl scale deployment ... --replicas=N) or automatic using theHorizontal Pod Autoscaler (HPA). HPA adjusts replicas based on observed metrics like CPU utilization, memory, or custom/external metrics (for example, request rate or queue length). This creates responsive systems that can handle variable traffic.

From an architecture perspective, designing for horizontal scaling often means ensuring your application is stateless (or manages state externally), uses idempotent request handling, and supports multiple concurrent instances. Stateful workloads can also scale horizontally, but usually with additional constraints (StatefulSets, sharding, quorum membership, stable identity).

So the verified definition and correct choice isC.

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A Helm chart is best described as apackagefor Kubernetes applications, containing the resource definitions (as templates) and metadata needed to install and manage an application—soDis correct. Helm is a package manager for Kubernetes; the chart is the packaging format. Charts include a Chart.yaml (metadata), a values.yaml (default configuration values), and a templates/ directory containing Kubernetes manifests written as templates. When you install a chart, Helm renders those templates into concrete Kubernetes YAML manifests by substituting values, then applies them to the cluster.

Option A is misleading/incomplete. While charts are often distributed as a compressed tarball (.tgz), the defining feature is not “YAML bundled in tar.gz” but the packaging and templating model that supports install/upgrade/rollback. Option B is incorrect because Helm charts are not “collections of JSON files” by definition; Kubernetes resources can be expressed as YAML or JSON, but Helm charts overwhelmingly use templated YAML. Option C is incorrect because charts are not simply YAML applied by kubectl; Helm manages releases, tracks installed resources, and supports upgrades and rollbacks. Helm uses Kubernetes APIs under the hood, but the value of Helm is the lifecycle and packaging system, not “kubectl apply.”

In cloud-native application delivery, Helm helps standardize deployments across environments (dev/stage/prod) by externalizing configuration through values. It reduces copy/paste and supports reuse via dependencies and subcharts. Helm also supports versioning of application packages, allowing teams to upgrade predictably and roll back if needed—critical for production change management.

So, the correct and verified definition isD: a Helm chart is like a package containing the resource definitions needed to run an application on Kubernetes.

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A KubernetesService without selectorsrequires you to manage its backend endpoints manually, soBis correct. Normally, a Service uses aselectorto match a set of Pods (by labels). Kubernetes then automatically maintains the backend list (historically Endpoints, now commonly EndpointSlice) by tracking which Pods match the selector and are Ready. This automation is one of the key reasons Services provide stable connectivity to dynamic Pods.

When you create a Servicewithout a selector, Kubernetes has no way to know which Pods (or external IPs) should receive traffic. In that pattern, you explicitly create anEndpointsobject (or EndpointSlices, depending on your approach and controller support) that maps the Service name to one or more IP:port tuples. This is commonly used to representexternal services(e.g., a database running outside the cluster) while still providing a stable Kubernetes Service DNS name for in-cluster clients. Another use case is advanced migration scenarios where endpoints are controlled by custom controllers rather than label selection.

Why the other options are wrong: ServicetypeslikeClusterIP,NodePort, andLoadBalancerdescribe how a Service is exposed, but they do not inherently require manual endpoint management. A ClusterIP Servicewith selectors(D) is the standard case where endpoints are automatically created and updated. NodePort and LoadBalancer Services also typically use selectors and therefore inherit automatic endpoint management; the difference is in how traffic enters the cluster, not how backends are discovered.

Operationally, when using Services without selectors, you must ensure endpoint IPs remain correct, health is accounted for (often via external tooling), and you update endpoints when backends change. The key concept is:no selector → Kubernetes can’t auto-populate endpoints → you must provide them.

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