Pearson KCNA New Attempt

Argo CD is a declarative GitOps continuous delivery tool designed to manage Kubernetes applications across one or many clusters. When the requirement is to automatically deploy a microservice to every cluster registered in Argo CD, the most appropriate and scalable solution is to use an ApplicationSet.

The ApplicationSet controller extends Argo CD by enabling the dynamic generation of multiple Argo CD Applications from a single template. One of its most powerful features is the cluster generator, which automatically discovers all clusters registered with Argo CD and creates an Application for each of them. By combining this generator with a Git repository containing the microservice manifests, the platform engineer ensures that the microservice is consistently deployed to all existing clusters—and any new clusters added in the future—without manual intervention.

This approach aligns perfectly with GitOps principles. The desired state of the microservice is defined once in Git, and Argo CD continuously reconciles that state across all target clusters. Any updates to the microservice manifests are automatically rolled out everywhere in a controlled and auditable manner. This provides strong guarantees around consistency, scalability, and operational simplicity.

Option A is incorrect because a CronJob introduces imperative redeployment logic and does not integrate with Argo CD’s reconciliation model. Option B is not scalable or maintainable, as it requires manual configuration for each cluster and increases the risk of configuration drift. Option D, while useful for packaging applications, still results in a single Application object and does not natively handle multi-cluster fan-out by itself.

Therefore, the correct and verified answer is Option C: creating an Argo CD ApplicationSet backed by a Git repository, which is the recommended and documented solution for multi-cluster application delivery in Argo CD.

The correct answer is C: Retain. In Kubernetes persistent storage, a PersistentVolume (PV) has a persistentVolumeReclaimPolicy that determines what happens to the underlying storage asset after its PersistentVolumeClaim (PVC) is deleted. The reclaim policy options historically include Delete and Retain (and Recycle, which is deprecated/removed in many modern contexts). “Manual reclamation” refers to the administrator having to manually clean up and/or rebind the storage after the claim is released—this behavior corresponds to Retain.

With Retain, when the PVC is deleted, the PV moves to a “Released” state, but the actual storage resource (cloud disk, NFS path, etc.) is not deleted automatically. Kubernetes will not automatically make that PV available for a new claim until an administrator takes action—typically cleaning the data, removing the old claim reference, and/or creating a new PV/PVC binding flow. This is important for data safety: you don’t want to automatically delete sensitive or valuable data just because a claim was removed.

By contrast, Delete means Kubernetes (via the storage provisioner/CSI driver) will delete the underlying storage asset when the claim is deleted—useful for dynamic provisioning and disposable environments. Recycle used to scrub the volume contents and make it available again, but it’s not the recommended modern approach and has been phased out in favor of dynamic provisioning and explicit workflows.

So, the policy that implies manual intervention and manual cleanup/reuse is Retain, which is option C.

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Rook is the open-source, cloud-native storage orchestrator specifically designed to automate the deployment, configuration, and lifecycle management of Ceph within Kubernetes environments. Its primary goal is to transform complex, traditionally manual storage systems like Ceph into Kubernetes-native services that are easy to operate and highly resilient.

Ceph itself is a mature and powerful distributed storage platform that supports block storage (RBD), object storage (RGW), and shared filesystems (CephFS). However, operating Ceph directly requires deep expertise, careful configuration, and continuous operational management. Rook addresses this challenge by running Ceph as a set of Kubernetes-managed components and exposing storage capabilities through Kubernetes Custom Resource Definitions (CRDs). This allows administrators to declaratively define storage clusters, pools, filesystems, and object stores using familiar Kubernetes patterns.

Rook continuously monitors the health of the Ceph cluster and takes automated actions to maintain the desired state. If a Ceph daemon fails or a node becomes unavailable, Rook works with Kubernetes scheduling and Ceph’s internal replication mechanisms to ensure data durability and service continuity. This enables self-healing behavior. Scaling storage capacity is also simplified—adding nodes or disks allows Rook and Ceph to automatically rebalance data, providing self-scaling capabilities without manual intervention.

The other options are incorrect for this use case. CubeFS is a distributed filesystem but is not a Ceph orchestrator. OpenEBS focuses on container-attached storage and local or replicated volumes rather than managing Ceph itself. MinIO is an object storage server compatible with S3 APIs, but it does not orchestrate Ceph or provide block and filesystem services.

Therefore, the correct and verified answer is Option C: Rook, which is the officially recognized Kubernetes-native orchestrator for Ceph, delivering automated, resilient, and scalable storage management aligned with cloud-native principles.

The best answer is B (resiliency). A microservices-centered cloud-native architecture is designed to build systems that continue to operate effectively under change and failure. “Resiliency” is the umbrella concept: the ability to tolerate faults, recover from disruptions, and maintain acceptable service levels through redundancy, isolation, and automated recovery.

Microservices help resiliency by reducing blast radius. Instead of one monolith where a single defect can take down the entire application, microservices separate concerns into independently deployable components. Combined with Kubernetes, you get resiliency mechanisms such as replication (multiple Pod replicas), self-healing (restart and reschedule on failure), rolling updates, health probes, and service discovery/load balancing. These enable the platform to detect and replace failing instances automatically, and to keep traffic flowing to healthy backends.

Options C (failover) and A (fallback) are resiliency techniques but are narrower terms. Failover usually refers to switching to a standby component when a primary fails; fallback often refers to degraded behavior (cached responses, reduced features). Both can exist in microservice systems, but the broader architectural guarantee microservices aim to support is resiliency overall. Option D (“high reachability”) is not the standard term used in cloud-native design and doesn’t capture the intent as precisely as resiliency.

In practice, achieving resiliency also requires good observability and disciplined delivery: monitoring/alerts, tracing across service boundaries, circuit breakers/timeouts/retries, and progressive delivery patterns. Kubernetes provides platform primitives, but resilient microservices also need careful API design and failure-mode thinking.

So the intended and verified completion is resiliency, option B.

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