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| Section | Objectives |
|---|---|
| Cloud Security and Governance | - Security fundamentals in cloud native systems
|
| Cloud Native Architecture | - Microservices and distributed systems
|
| Kubernetes Fundamentals | - Core Kubernetes concepts
|
| Cloud Native Observability | - Monitoring and logging
|
| Cloud Native Application Delivery | - CI/CD and deployment strategies
|
| Container Orchestration | - Container lifecycle and scheduling
|
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NEW QUESTION # 72
Which of these is a valid container restart policy?
Answer: D
NEW QUESTION # 73
How does dynamic storage provisioning work?
Answer: A
Explanation:
Dynamic provisioning is the Kubernetes mechanism where storage is created on-demand when a user creates a PersistentVolumeClaim (PVC) that references a StorageClass, so A is correct. In this model, the user does not need to pre-create a PersistentVolume (PV). Instead, the StorageClass points to a provisioner (typically a CSI driver) that knows how to create a volume in the underlying storage system (cloud disk, SAN, NAS, etc.). When the PVC is created with storageClassName: <class>, Kubernetes triggers the provisioner to create a new volume and then binds the resulting PV to that PVC.
This is why option B is incorrect: you do not put a StorageClass "in the Pod YAML" to request provisioning. Pods reference PVCs, not StorageClasses directly. Option C is incorrect because the PVC does not need the Pod name; binding is done via the PVC itself. Option D describes static provisioning: an admin pre-creates PVs and users claim them by creating PVCs that match the PV (capacity, access modes, selectors). Static provisioning can work, but it is not dynamic provisioning.
Under the hood, the StorageClass can define parameters like volume type, replication, encryption, and binding behavior (e.g., volumeBindingMode: WaitForFirstConsumer to delay provisioning until the Pod is scheduled, ensuring the volume is created in the correct zone). Reclaim policies (Delete/Retain) define what happens to the underlying volume after the PVC is deleted.
In cloud-native operations, dynamic provisioning is preferred because it improves developer self-service, reduces manual admin work, and makes scaling stateful workloads easier and faster. The essence is: PVC + StorageClass → automatic PV creation and binding.
NEW QUESTION # 74
In a Kubernetes cluster, what is the primary role of the Kubernetes scheduler?
Answer: D
Explanation:
The Kubernetes scheduler is a core control plane component responsible for deciding where Pods should run within a cluster. Its primary role is to assign newly created Pods that do not yet have a node assigned to an appropriate node based on a variety of factors such as resource availability, scheduling constraints, and policies.
When a Pod is created, it enters a Pending state until the scheduler selects a suitable node. The scheduler evaluates all available nodes and filters out those that do not meet the Pod's requirements. These requirements may include CPU and memory requests, node selectors, node affinity rules, taints and tolerations, topology spread constraints, and other scheduling policies. After filtering, the scheduler scores the remaining nodes to determine the best placement for the Pod and then binds the Pod to the selected node.
Option A is incorrect because restarting failed Pods is handled by other components such as the kubelet and higher-level controllers like Deployments, ReplicaSets, or StatefulSets-not the scheduler. Option B is incorrect because monitoring node and Pod health is primarily the responsibility of the kubelet and the Kubernetes controller manager, which reacts to node failures and ensures desired state. Option C is incorrect because handling network traffic is managed by Services, kube-proxy, and the cluster's networking implementation, not the scheduler.
Option D correctly describes the scheduler's purpose. By distributing Pods across nodes based on resource availability and constraints, the scheduler helps ensure efficient resource utilization, high availability, and workload isolation. This intelligent placement is essential for maintaining cluster stability and performance, especially in large-scale or multi-tenant environments.
According to Kubernetes documentation, the scheduler's responsibility is strictly focused on Pod placement decisions. Once a Pod is scheduled, the scheduler's job is complete for that Pod, making option D the accurate and fully verified answer.
NEW QUESTION # 75
How does Horizontal Pod autoscaling work in Kubernetes?
Answer: C
Explanation:
Horizontal Pod Autoscaling (HPA) adjusts the number of Pod replicas for a workload controller (most commonly a Deployment) based on observed metrics, increasing replicas when load is high and decreasing when load drops. That matches D, so D is correct.
HPA does not add CPU or memory to existing Pods-that would be vertical scaling (VPA). Instead, HPA changes spec.replicas on the target resource, and the controller then creates or removes Pods accordingly.
HPA commonly scales based on CPU utilization and memory (resource metrics), and it can also scale using custom or external metrics if those are exposed via the appropriate Kubernetes metrics APIs.
Option A is vertical scaling behavior, not HPA. Option B is incorrect because HPA can scale down as well as up (subject to stabilization windows and configuration), so it's not "scale up only." Option C is incorrect because HPA does not scale DaemonSets in the usual model; DaemonSets are designed to run one Pod per node (or per selected nodes) rather than a replica count. HPA targets resources like Deployments, ReplicaSets (via Deployment), and StatefulSets in typical usage, where replica count is a meaningful knob.
Operationally, HPA works as a control loop: it periodically reads metrics (for example, via metrics-server for CPU/memory, or via adapters for custom metrics), compares the current value to the desired target, and calculates a desired replica count within min/max bounds. To avoid flapping, HPA includes stabilization behavior and cooldown logic so it doesn't scale too aggressively in response to short spikes or dips. You can configure minimum and maximum replicas and behavior policies to tune responsiveness.
In cloud-native systems, HPA is a key elasticity mechanism: it enables services to handle variable traffic while controlling cost by scaling down during low demand. Therefore, the verified correct answer is D.
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NEW QUESTION # 76
You are running a large-scale, microservice-based application in Kubernetes, and you are using HorizontalPodAutoscalers (HPAs) for automatic scaling. Which of the following statements about resource utilization monitoring in this scenario is true?
Answer: D
Explanation:
Monitoring resource utilization at both the Pod and namespace levels provides a comprehensive understanding of resource usage. Monitoring Pods allows you to identify resource-intensive services and fine-tune their scaling behavior. Monitoring namespaces helps you understand the overall resource consumption and optimize scaling across the entire application. This is crucial in large-scale microservice deployments where resources can be easily exhausted if not carefully monitored
NEW QUESTION # 77
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