KCNA Exam Dumps - Kubernetes and Cloud Native Associate

The role most commonly responsible for defining, testing, and running anincident managementprocess isSite Reliability Engineers (SREs), soAis correct. SRE is an operational engineering discipline focused on ensuring reliability, availability, and performance of services in production. Incident management is a core part of that mission: when outages or severe degradations occur, someone must coordinate response, restore service quickly, and then drive follow-up improvements to prevent recurrence.

In cloud native environments (including Kubernetes), incident response involves both technical and process elements. On the technical side, SREs ensure observability is in place—metrics, logs, traces, dashboards, and actionable alerts—so incidents can be detected and diagnosed quickly. They also validate operational readiness: runbooks, escalation paths, on-call rotations, and post-incident review practices. On the process side, SREs often establish severity classifications, response roles (incident commander, communications lead, subject matter experts), and “game day” exercises or simulated incidents to test preparedness.

Project managers may help coordinate schedules and communication for projects, but they are not typically the owners of operational incident response mechanics. Application developers are crucial participants during incidents, especially for debugging application-level failures, but they are not usually the primary maintainers of the incident management framework. Quality engineers focus on testing and quality assurance, and while they contribute to preventing defects, they are not usually the owners of real-time incident operations.

In Kubernetes specifically, incidents often span multiple layers: workload behavior, cluster resources, networking, storage, and platform dependencies. SREs are positioned to manage the cross-cutting operational view and to continuously improve reliability through error budgets, SLOs/SLIs, and iterative hardening. That’s why the correct persona isSite Reliability Engineers.

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The correct answer isA. In cloud-native environments, costs are highly dynamic: autoscaling changes compute footprint, ephemeral environments come and go, and usage-based billing applies to storage, network egress, load balancers, and observability tooling. Because of this variability,automationis the most sustainable way to achieve cost efficiency. Automated visibility (dashboards, chargeback/showback), anomaly detection, and forecasting help teams understand where spend is coming from and how it changes over time. Automated optimization actions can include right-sizing requests/limits, enforcing TTLs on preview environments, scaling down idle clusters, and cleaning unused resources.

Manual processes (B) don’t scale as complexity grows. By the time someone reviews a spreadsheet or dashboard weekly, cost spikes may have already occurred. Automation enables fast feedback loops and guardrails, which is essential for preventing runaway spend caused by misconfiguration (e.g., excessive log ingestion, unbounded autoscaling, oversized node pools).

Option C is not a cost-efficiency “habit.” Single-provider strategies may simplify some billing views, but they can also reduce leverage and may not be feasible for resilience/compliance; it’s a business choice, not a best practice for cloud-native cost management. Option D is counterproductive: keeping legacy workloads unchanged often wastes money because cloud efficiency typically requires adapting workloads—right-sizing, adopting autoscaling, and using managed services appropriately.

In Kubernetes specifically, cost efficiency is tightly linked to resource management: accurate CPU/memory requests, limits where appropriate, cluster autoscaler tuning, and avoiding overprovisioning. Observability also matters because you can’t optimize what you can’t measure. Therefore, the best habit is anautomatedcost optimization approach with strong visibility and forecasting—A.

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Kubernetes stores its cluster state (API objects) inetcd, makingAcorrect. etcd is a distributed, strongly consistent key-value store that serves as the source of truth for the Kubernetes control plane. When you create or update objects such as Pods, Deployments, ConfigMaps, Secrets, or Nodes, thekube-apiservervalidates the request and then persists the desired state into etcd. Controllers and the scheduler watch the API for changes (which ultimately reflect etcd state) and reconcile the cluster to match that desired state.

etcd’s consistency guarantees are crucial. Kubernetes relies on accurate, up-to-date state to make scheduling decisions, enforce RBAC/admission policies, coordinate leader elections, and ensure controllers behave correctly. etcd uses the Raft consensus algorithm to replicate data among members and requires quorum for writes, enabling fault tolerance when deployed in HA configurations (commonly three or five members).

The other options are incorrect in Kubernetes’ standard architecture. ZooKeeper is a distributed coordination system used by some other platforms, but Kubernetes does not use it as its primary datastore. Redis is an in-memory data store used for caching or messaging, not as Kubernetes’ authoritative state store. “ControlPlaneStore” is not a standard Kubernetes component.

Operationally, etcd health is one of the most important determinants of cluster reliability. Slow disk I/O or unstable networking can degrade etcd performance and cause API latency spikes. Backup and restore procedures for etcd are critical disaster-recovery practices, and securing etcd (TLS, access restrictions) is essential because it may contain sensitive data (e.g., Secrets—often base64-encoded, and optionally encrypted at rest depending on configuration).

Therefore, the verified Kubernetes datastore isetcd, optionA.

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In Kubernetes, Pods typically attach persistent storage by referencing aPersistentVolumeClaim (PVC), makingDcorrect. A PVC is a user’s request for storage with specific requirements (size, access mode, storage class). Kubernetes then binds the PVC to a matchingPersistentVolume (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: , 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 isPersistentVolumeClaim, optionD.

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Any Kubernetes object manifest must includeapiVersion,kind, andmetadata, which makesAcorrect. This comes directly from how Kubernetes resources are represented and processed by the API server.

apiVersiontells Kubernetes which API group and version should be used to interpret the object (for example v1, apps/v1, batch/v1). This matters because schemas and available fields can change between versions.

kindspecifies the type of object you are creating (for example Pod, Service, Deployment, ConfigMap). Kubernetes uses this to route the request to the correct API endpoint and schema.

metadatacontains identifying and organizational information such as name, namespace (when namespaced), labels, and annotations. At minimum, most objects require a name; labels and annotations are optional but extremely common for selection and tooling.

A common point of confusion isspec. Many Kubernetes objects include spec because they define desired state (like a Deployment’s replica count, Pod template, update strategy). However, the question asks what fields must exist inanyKubernetes object file. Not all objects require a spec in the same way (and some objects include other top-level sections like data for ConfigMaps/Secrets or rules for RBAC objects). The truly universal top-level requirements are the trio in option A.

Options B, C, and D include fields that are not universally required (namespace is not required for cluster-scoped objects, and data only applies to certain kinds like ConfigMaps/Secrets). Therefore,apiVersion + kind + metadatais the correct, general rule and matches Kubernetes object structure.

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The correct answer isB: Continuous Integration (CI) is the practice of frequently integrating code changes from multiple contributors and validating them throughautomatedbuilds and tests. The “continuous” part is about doing this often (ideally many times per day) and consistently, so integration problems are detected early instead of piling up until a painful merge or release window.

Automation is essential. CI typically includes steps like compiling/building artifacts, running unit and integration tests, executing linters, checking formatting, scanning dependencies for vulnerabilities, and producing build reports. This automation creates fast feedback loops that help developers catch regressions quickly and maintain a releasable main branch.

Option A is incorrect because manual integration/testing does not scale and undermines the reliability and speed that CI is meant to provide. Option C confuses CI with deployment promotion across environments (which is more aligned with Continuous Delivery/Deployment). Option D is unrelated: adding tools can support CI, but it isn’t the definition.

In cloud-native application delivery, CI is tightly coupled with containerization and Kubernetes: CI pipelines often build container images from source, run tests, scan images, sign artifacts, and push to registries. Those validated artifacts then flow into CD processes that deploy to Kubernetes using manifests, Helm, or GitOps controllers. Without CI, Kubernetes rollouts become riskier because you lack consistent validation of what you’re deploying.

So, CI is best defined asautomated integration and testing of code changes from multiple sources, which matches optionB.

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Serverless architectures typically bill based onactual consumption, often measured as number of requests and execution duration (and sometimes memory/CPU allocated), soAis correct. The defining trait is that you don’t provision or manage servers directly; the platform scales execution up and down automatically, including down to zero for many models, and charges you for what you use.

Option B is incorrect: many serverless platforms can run container-based workloads (and some are explicitly “serverless containers”). The idea is the operational abstraction and billing model, not incompatibility with containers. Option C is incorrect because “making a reservation based on estimation” describes reserved capacity purchasing, which is the opposite of the typical serverless pay-per-use model. Option D is misleading: serverless systems aim to avoid charging for idle compute; while platforms may keep some warm capacity for latency reasons, the customer-facing model is not “containers running idle in the background.”

In cloud-native architecture, serverless is often chosen for spiky, event-driven workloads where you want minimal ops overhead and cost efficiency at low utilization. It pairs naturally with eventing systems (queues, pub/sub) and can be integrated with Kubernetes ecosystems via event-driven autoscaling frameworks or managed serverless offerings.

So the correct statement isA: charging is commonly based on requests (and usage), which captures the cost and operational model that differentiates serverless from always-on infrastructure.

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ADockerfileis atext filethat contains a sequence of instructions used to build a container image, soCis correct. These instructions include choosing a base image (FROM), copying files (COPY/ADD), installing dependencies (RUN), setting environment variables (ENV), defining working directories (WORKDIR), exposing ports (EXPOSE), and specifying the default startup command (CMD/ENTRYPOINT). When you run docker build (or compatible tools like BuildKit), the builder executes these instructions to produce an image composed of immutable layers.

In cloud-native application delivery, Dockerfiles (more generally, OCI image build definitions) are a key step in the supply chain. The resulting image artifact is what Kubernetes runs in Pods. Best practices include using minimal base images, pinning versions, avoiding embedding secrets, and using multi-stage builds to keep runtime images small. These practices improve security and performance, and make delivery pipelines more reliable.

Option A is incorrect because a Dockerfile is not a bash script, even though it can run shell commands through RUN. Option B is incorrect because registry destinations are handled by tooling and tagging/push commands (or CI pipeline configuration), not by the Dockerfile itself. Option D is incorrect because an image layer created by a running container is more closely related to container filesystem changes and commits; a Dockerfile is the build recipe, not a runtime-generated layer.

Although the question uses “Dockerfile,” the concept maps well to OCI-based container image creation generally: you define a reproducible build recipe that produces an immutable image artifact. That artifact is then versioned, scanned, signed, stored in a registry, and deployed to Kubernetes through manifests/Helm/GitOps. Therefore,Cis the correct and verified definition.

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