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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Cloud Native Observability | 8% | - Logging & Tracing
|
| Topic 2: Container Orchestration | 22% | - Security & Troubleshooting
|
| Topic 3: Kubernetes Fundamentals | 46% | - Containerization Basics
|
| Topic 4: Cloud Native Application Delivery | 8% | - Deployment Strategies
|
| Topic 5: Cloud Native Architecture | 16% | - Ecosystem & Landscape
|
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NEW QUESTION # 140
Manual reclamation policy of a PV resource is known as:
Answer: C
Explanation:
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.
NEW QUESTION # 141
Which of the following is a lightweight tool that manages traffic flows between services, enforces access policies, and aggregates telemetry data, all without requiring changes to application code?
Answer: A
Explanation:
Linkerd is a lightweight service mesh that manages service-to-service traffic, security policies, and telemetry without requiring application code changes-so B is correct. A service mesh introduces a dedicated layer for east-west traffic (internal service calls) and typically provides features like mutual TLS (mTLS), retries
/timeouts, traffic shaping, and consistent metrics/tracing signals. Linkerd is known for being simpler and resource-efficient relative to some alternatives, which aligns with the "lightweight tool" phrasing.
Why this matches the description: In a service mesh, workload traffic is intercepted by a proxy layer (often as a sidecar or node-level/ambient proxy) and managed centrally by mesh control components. This allows security and traffic policy to be applied uniformly without modifying each microservice. Telemetry is also generated consistently because the proxies observe traffic directly and emit metrics and traces about request rates, latency, and errors.
The other choices don't fit. NetworkPolicy is a Kubernetes resource that controls allowed network flows (L3
/L4) but does not provide L7 traffic management, retries, identity-based mTLS, or automatic telemetry aggregation. kube-proxy implements Service networking rules (ClusterIP/NodePort forwarding) but does not enforce access policies at the service identity level and is not a telemetry system. Nginx can be used as an ingress controller or reverse proxy, but it is not inherently a full service mesh spanning all service-to-service communication and policy/telemetry across the mesh by default.
In cloud native architecture, service meshes help address cross-cutting concerns-security, observability, and traffic management-without embedding that logic into every application. The question's combination of
"traffic flows," "access policies," and "aggregates telemetry" maps directly to a mesh, and the lightweight mesh option provided is Linkerd.
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NEW QUESTION # 142
What best describes cloud native service discovery?
Answer: A
Explanation:
Cloud native service discovery is fundamentally about how services and microservices find and connect to each other reliably in a dynamic environment, so A is correct. In cloud native systems (especially Kubernetes), instances are ephemeral: Pods can be created, destroyed, rescheduled, and scaled at any time.
Hardcoding IPs breaks quickly. Service discovery provides stable names and lookup mechanisms so that one component can locate another even as underlying endpoints change.
In Kubernetes, service discovery is commonly achieved through Services (stable virtual IP + DNS name) and cluster DNS (CoreDNS). A Service selects a group of Pods via labels, and Kubernetes maintains the set of endpoints behind that Service. Clients connect to the Service name (DNS) and Kubernetes routes traffic to the current healthy Pods. For some workloads, headless Services provide DNS records that map directly to Pod IPs for per-instance discovery.
The other options describe different networking concepts: B is ARP (MAC discovery), C is DHCP (IP assignment), and D is DNS in a general internet sense. DNS is often used as a mechanism for service discovery, but cloud native service discovery is broader: it's the overall mechanism enabling dynamic location of services, often implemented via DNS and/or environment variables and sometimes enhanced by service meshes.
So the best description remains A: a mechanism that allows applications and microservices to locate each other on a network in a dynamic environment.
NEW QUESTION # 143
What does the "nodeSelector" within a PodSpec use to place Pods on the target nodes?
Answer: A
Explanation:
nodeSelector is a simple scheduling constraint that matches node labels, so the correct answer is D (Labels). In Kubernetes, nodes have key/value labels (for example, disktype=ssd, topology.kubernetes.io/zone=us-east-1a, kubernetes.io/os=linux). When you set spec.nodeSelector in a Pod template, you provide a map of required label key/value pairs. The kube-scheduler will then only consider nodes that have all those labels with matching values as eligible placement targets for that Pod.
This is different from annotations: annotations are also key/value metadata, but they are not intended for selection logic and are not used by the scheduler for nodeSelector. IP addresses and hostnames are not the mechanism used by nodeSelector either. While Kubernetes nodes do have hostnames and IPs, nodeSelector specifically operates on labels because labels are designed for selection, grouping, and placement constraints.
Operationally, nodeSelector is the most basic form of node placement control. It is commonly used to pin workloads to specialized hardware (GPU nodes), compliance zones, or certain OS/architecture pools. However, it has limitations: it only supports exact match on labels and cannot express more complex rules (like "in this set of zones" or "prefer but don't require"). For that, Kubernetes offers node affinity (requiredDuringSchedulingIgnoredDuringExecution, preferredDuringSchedulingIgnoredDuringExecution) which supports richer expressions.
Still, the underlying mechanism is the same concept: the scheduler evaluates your Pod's placement requirements against node metadata, and for nodeSelector, that metadata is labels. Therefore, the verified correct answer is D.
NEW QUESTION # 144
Which persona is normally responsible for defining, testing, and running an incident management process?
Answer: B
Explanation:
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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NEW QUESTION # 145
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