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Linux Foundation Kubernetes and Cloud Native Associate Sample Questions (Q23-Q28):

NEW QUESTION # 23
How many hosts are required to set up a highly available Kubernetes cluster when using an external etcd topology?

Answer: D

Explanation:
In a highly available (HA) Kubernetes control plane using an external etcd topology, you typically run three control plane nodes and three separate etcd nodes, totaling six hosts, making D correct. HA design relies on quorum-based consensus: etcd uses Raft and requires a majority of members available to make progress.
Running three etcd members is the common minimum for HA because it tolerates one member failure while maintaining quorum (2/3).
In the external etcd topology, etcd is decoupled from the control plane nodes. This separation improves fault isolation: if a control plane node fails or is replaced, etcd remains stable and independent; likewise, etcd maintenance can be handled separately. Kubernetes API servers (often multiple instances behind a load balancer) talk to the external etcd cluster for storage of cluster state.
Options A and B propose four hosts, but they break common HA/quorum best practices. Two etcd nodes do not form a robust quorum configuration (a two-member etcd cluster cannot tolerate a single failure without losing quorum). One control plane node is not HA for the API server/scheduler/controller-manager components. Option C describes a stacked etcd topology (control plane + etcd on same hosts), which can be HA with three hosts, but the question explicitly says external etcd, not stacked. In stacked topology, you often use three control plane nodes each running an etcd member. In external topology, you use three control plane + three etcd.
Operationally, external etcd topology is often used when you want dedicated resources, separate lifecycle management, or stronger isolation for the datastore. It can reduce blast radius but increases infrastructure footprint and operational complexity (TLS, backup/restore, networking). Still, for the canonical HA external- etcd pattern, the expected answer is six hosts: 3 control plane + 3 etcd.
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NEW QUESTION # 24
Which mechanism can be used to automatically adjust the amount of resources for an application?

Answer: A

Explanation:
The verified answer in the PDF is A (HPA), and that aligns with the common Kubernetes meaning of "adjust resources for an application" by scaling replicas. The Horizontal Pod Autoscaler automatically changes the number of Pod replicas for a workload (typically a Deployment) based on observed metrics such as CPU utilization, memory (in some configurations), or custom/external metrics. By increasing replicas under load, the application gains more total CPU/memory capacity available across Pods; by decreasing replicas when load drops, it reduces resource consumption and cost.
It's important to distinguish what each mechanism adjusts:
* HPA adjusts replica count (horizontal scaling).
* VPA adjusts Pod resource requests/limits (vertical scaling), which is literally "amount of CPU
/memory per pod," but it often requires restarts to apply changes depending on mode.
* Cluster Autoscaler adjusts the number of nodes in the cluster, not application replicas.
* KEDA is event-driven autoscaling that often drives HPA behavior using external event sources (queues, streams), but it's not the primary built-in mechanism referenced in many foundational Kubernetes questions.
Given the wording and the provided answer key, the intended interpretation is: "automatically adjust the resources available to the application" by scaling out/in the number of replicas. That's exactly HPA's role.
For example, if CPU utilization exceeds a target (say 60%), HPA computes a higher desired replica count and updates the workload. The Deployment then creates more Pods, distributing load and increasing available compute.
So, within this question set, the verified correct choice is A (Horizontal Pod Autoscaler).
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NEW QUESTION # 25
Services and Pods in Kubernetes are ______ objects.

Answer: A

Explanation:
In Kubernetes, resources like Pods and Services are represented as API objects that you create, read, update, delete, and watch via the Kubernetes RESTful API. That makes D (REST) the correct answer.
Kubernetes is fundamentally API-driven: the API server exposes endpoints for each resource type (for example, /api/v1/namespaces/{ns}/pods and /api/v1/namespaces/{ns}/services). Clients such as kubectl, controllers, operators, and external systems interact with these resources by making REST-style calls using HTTP verbs (GET, POST, PUT/PATCH, DELETE) and using watch streams for event-driven updates. This API-first design is what enables Kubernetes' declarative model-users submit desired state to the API server, and controllers reconcile the cluster to that desired state.
Options A and B (JSON and YAML) are common serialization formats used to represent Kubernetes objects, but they are not what the objects "are." Kubernetes objects are logical API resources; they can be encoded as JSON (what the API uses) and often authored as YAML for human convenience. YAML is effectively a superset-friendly format that can be converted to JSON. The underlying API object model remains the same regardless of whether you wrote YAML or JSON. Option C (Java) is unrelated; Java is a programming language that can interact with Kubernetes via client libraries, but Kubernetes objects are not
"Java objects" in the platform's definition.
So the accurate statement is: Pods and Services are Kubernetes REST API objects (resources) exposed and managed through the Kubernetes API server, which is why REST is the correct fill-in.
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NEW QUESTION # 26
What does the "nodeSelector" within a PodSpec use to place Pods on the target nodes?

Answer: B

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.
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NEW QUESTION # 27
Which of the following is a feature Kubernetes provides by default as a container orchestration tool?

Answer: C

Explanation:
Kubernetes provides automated rollouts and rollbacks for workloads by default (via controllers like Deployments), so D is correct. In Kubernetes, application delivery is controller-driven: you declare the desired state (new image, new config), and controllers reconcile the cluster toward that state. Deployments implement rolling updates, gradually replacing old Pods with new ones while respecting availability constraints. Kubernetes tracks rollout history and supports rollback to previous ReplicaSets when an update fails or is deemed unhealthy.
This is a core orchestration capability: it reduces manual intervention and makes change safer. Rollouts use readiness checks and update strategies to avoid taking the service down, and kubectl rollout status/history
/undo supports day-to-day release operations.
The other options are not "default Kubernetes orchestration features":
* Kubernetes is not a portable operating system (A). It's a platform for orchestrating containers on top of an OS.
* Kubernetes does not provide filesystem redundancy by itself (B). Storage redundancy is handled by underlying storage systems and CSI drivers (e.g., replicated block storage, distributed filesystems).
* Kubernetes does not include a built-in container image registry (C). You use external registries (Docker Hub, ECR, GCR, Harbor, etc.). Kubernetes pulls images but does not host them as a core feature.
So the correct "provided by default" orchestration feature in this list is the ability to safely manage application updates via automated rollouts and rollbacks.
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NEW QUESTION # 28
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