Passed Exam Today KCNA

A widely referenced interoperability standard in the service mesh ecosystem is the Service Mesh Interface (SMI), so C is correct. SMI was created to provide a common set of APIs for basic service mesh capabilities—helping users avoid being locked into a single mesh implementation for core features. While service meshes differ in architecture and implementation (e.g., Istio, Linkerd, Consul), SMI aims to standardize how common behaviors are expressed.

In cloud native architecture, service meshes address cross-cutting concerns for service-to-service communication: traffic policies, observability, and security (mTLS, identity). Rather than baking these concerns into every application, a mesh typically introduces data-plane proxies and a control plane to manage policy and configuration. SMI sits above those implementations as a common API model.

The other options are not commonly used industry standards. You may see other efforts and emerging APIs, but among the listed choices, SMI is the recognized standard name that appears in cloud native discussions and tooling integrations.

Also note a practical nuance: even with SMI, not every mesh implements every SMI spec fully, and many users still adopt mesh-specific CRDs and APIs for advanced features. But for this question’s framing—“common standard”—Service Mesh Interface is the correct answer.

The correct answer is A. A DaemonSet is a workload controller whose job is to ensure that a specific Pod runs on all nodes (or on a selected subset of nodes) in the cluster. This is fundamentally different from Deployments/ReplicaSets, which aim to maintain a certain replica count regardless of node count. With a DaemonSet, the number of Pods is implicitly tied to the number of eligible nodes: add a node, and the DaemonSet automatically schedules a Pod there; remove a node, and its Pod goes away.

DaemonSets are commonly used for node-level services and background agents: log collectors, node monitoring agents, storage daemons, CNI components, or security agents—anything where you want a presence on each node to interact with node resources. This aligns with option D’s phrasing (“agent on every node”), but option A is the canonical definition and is slightly broader because it covers “all or certain nodes” (via node selectors/affinity/taints-tolerations) and the fact that the unit is a Pod.

Why the other options are wrong: DaemonSets do not “keep kubelet running” (B); kubelet is a node service managed by the OS. DaemonSets do not use a replicas field to maintain a specific count (C); that’s Deployment/ReplicaSet behavior.

Operationally, DaemonSets matter for cluster operations because they provide consistent node coverage and automatically react to node pool scaling. They also require careful scheduling constraints so they land only where intended (e.g., only Linux nodes, only GPU nodes). But the main purpose remains: ensure a copy of a Pod runs on each relevant node—option A.

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The READY column in the output of kubectl get pods indicates how many containers in a Pod are currently considered ready to serve traffic, compared to the total number of containers defined in that Pod. A value of 0/1 means that the Pod has one container, but that container is not yet marked as ready. The Kubernetes feature responsible for determining this readiness state is the readiness probe.

Readiness probes are used by Kubernetes to decide when a container is ready to accept traffic. These probes can be configured to perform HTTP requests, execute commands, or check TCP sockets inside the container. If a readiness probe is defined and it fails, Kubernetes marks the container as not ready, even if the container is running successfully. As a result, the READY column will show 0/1, and the Pod will be excluded from Service load balancing until the probe succeeds.

Option A (Node Selector) is incorrect because node selectors influence where a Pod is scheduled, not whether its containers are considered ready after startup. Option C (DNS Policy) affects how DNS resolution works inside a Pod and has no direct impact on readiness reporting. Option D (Security Contexts) define security-related settings such as user IDs, capabilities, or privilege levels, but they do not control the READY status shown by kubectl.

Readiness probes are particularly important for applications that take time to initialize, load configuration, or warm up caches. By using readiness probes, Kubernetes ensures that traffic is only sent to containers that are fully prepared to handle requests. This improves reliability and prevents failed or premature connections.

According to Kubernetes documentation, a container without a readiness probe is considered ready by default once it is running. However, when a readiness probe is defined, its result directly controls the READY state. Therefore, the presence and behavior of readiness probes is the verified and correct reason why a Pod may show 0/1 in the READY column, making option B the correct answer.

The role most commonly responsible for defining, testing, and running an incident management process is Site Reliability Engineers (SREs), so A is 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 is Site Reliability Engineers.

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