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| Section | Objectives |
|---|---|
| Topic 1: Cloud Native Observability | - Monitoring and logging
|
| Topic 2: Kubernetes Fundamentals | - Core Kubernetes concepts
|
| Topic 3: Cloud Native Architecture | - Microservices and distributed systems
|
| Topic 4: Cloud Native Application Delivery | - CI/CD and deployment strategies
|
| Topic 5: Cloud Security and Governance | - Security fundamentals in cloud native systems
|
| Topic 6: Container Orchestration | - Container lifecycle and scheduling
|
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NEW QUESTION # 148
What is the primary mechanism to identify grouped objects in a Kubernetes cluster?
Answer: A
Explanation:
Kubernetes groups and organizes objects primarily using labels, so B is correct. Labels are key-value pairs attached to objects (Pods, Deployments, Services, Nodes, etc.) and are intended to be used for identifying, selecting, and grouping resources in a flexible, user-defined way.
Labels enable many core Kubernetes behaviors. For example, a Service selects the Pods that should receive traffic by matching a label selector against Pod labels. A Deployment's ReplicaSet similarly uses label selectors to determine which Pods belong to the replica set. Operators and platform tooling also rely on labels to group resources by application, environment, team, or cost center. This is why labeling is considered foundational Kubernetes hygiene: consistent labels make automation, troubleshooting, and governance easier.
A "label selector" (option C) is how you query/group objects based on labels, but the underlying primary mechanism is still the labels themselves. Without labels applied to objects, selectors have nothing to match. Custom Resources (option A) extend the API with new kinds, but they are not the primary grouping mechanism across the cluster. "Pod" (option D) is a workload unit, not a grouping mechanism.
Practically, Kubernetes recommends common label keys like app.kubernetes.io/name, app.kubernetes.io/instance, and app.kubernetes.io/part-of to standardize grouping. Those conventions improve interoperability with dashboards, GitOps tooling, and policy engines.
So, when the question asks for the primary mechanism used to identify grouped objects in Kubernetes, the most accurate answer is Labels (B)-they are the universal metadata primitive used to group and select resources.
NEW QUESTION # 149
Which item is a Kubernetes node component?
Answer: B
Explanation:
A Kubernetes node component is a component that runs on worker nodes to support Pods and node-level networking/operations. Among the options, kube-proxy is a node component, so C is correct.
kube-proxy runs on each node and implements parts of the Kubernetes Service networking model. It watches the API server for Service and endpoint updates and then programs node networking rules (iptables/IPVS, or equivalent) so traffic sent to a Service IP/port is forwarded to one of the backend Pod endpoints. This is essential for stable virtual IPs and load distribution across Pods.
Why the other options are not node components:
kube-scheduler is a control plane component; it assigns Pods to nodes but does not run on every node as part of node functionality.
kubectl is a client CLI tool used by humans/automation; it is not a cluster component.
etcd is the control plane datastore; it stores cluster state and is not a per-node workload component.
Operationally, kube-proxy can be replaced by some modern CNI/eBPF dataplanes, but in classic Kubernetes architecture it remains the canonical node-level component for Service rule programming. Understanding which components are node vs control plane is key for troubleshooting: node issues involve kubelet/runtime/kube-proxy/CNI; control plane issues involve API server/scheduler/controller-manager/etcd.
So, the verified node component in this list is kube-proxy (C).
NEW QUESTION # 150
Which of the following options include resources cleaned by the Kubernetes garbage collection mechanism?
Answer: C
Explanation:
Kubernetes garbage collection (GC) is about cleaning up API objects and related resources that are no longer needed, so the correct answer is D. Two big categories it targets are (1) objects that have finished their lifecycle (like terminated Pods and completed Jobs, depending on controllers and TTL policies), and (2)
"dangling" objects that are no longer referenced properly-often described as objects without owner references (or where owners are gone), which can happen when a higher-level controller is deleted or when dependent resources are left behind.
A key Kubernetes concept here is OwnerReferences: many resources are created "owned" by a controller (e.
g., a ReplicaSet owned by a Deployment, Pods owned by a ReplicaSet). When an owning object is deleted, Kubernetes' garbage collector can remove dependent objects based on deletion propagation policies (foreground/background/orphan). This prevents resource leaks and keeps the cluster tidy and performant.
The other options are incorrect because they refer to cleanup tasks outside Kubernetes GC's scope. Kubelet logs (B/C) are node-level files and log rotation is handled by node/runtime configuration, not the Kubernetes garbage collector. Unused container images (C) are managed by the container runtime's image GC and kubelet disk pressure management, not the Kubernetes API GC. Nodes deleted by a cloud controller (B) aren' t "garbage collected" in the same sense; node lifecycle is handled by controllers and cloud integrations, but not as a generic GC cleanup category like ownerRef-based object deletion.
So, when the question asks specifically about "resources cleaned by Kubernetes garbage collection," it's pointing to Kubernetes object lifecycle cleanup: terminated Pods, completed Jobs, and orphaned objects- exactly what option D states.
=========
NEW QUESTION # 151
Your application is deployed in a Kubernetes cluster and is experiencing performance issues. You need to monitor the health of the cluster and identify potential bottlenecks. Which Kubernetes features would be most helpful for diagnosing and resolving these issues?
Answer: D
Explanation:
All of the mentioned features contribute to effective cluster monitoring and debugging: 'kubectl logs and 'kubectl describe' provide valuable information about specific Pods and their resources. Prometheus and Grafana allow you to collect and analyze metrics from various components of the cluster, helping you identify performance bottlenecks. Kubernetes Dashboard offers a visual overview of the cluster's state and resources. Jaeger is a powerful tool for tracing distributed requests across different microservices within your application, helping you diagnose issues that involve communication between services.
NEW QUESTION # 152
Which type of Service requires manual creation of Endpoints?
Answer: D
NEW QUESTION # 153
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