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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Kubernetes Fundamentals | 46% | - Scheduling and Administration
|
| Topic 2: Cloud Native Observability | 8% | - Monitoring & Metrics
|
| Topic 3: Container Orchestration | 22% | - Orchestration Principles
|
| Topic 4: Cloud Native Application Delivery | 8% | - Delivery Models
|
| Topic 5: Cloud Native Architecture | 16% | - Cloud Native Principles
|
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NEW QUESTION # 222
In a cloud native world, what does the IaC abbreviation stands for?
Answer: C
NEW QUESTION # 223
How do you perform a command in a running container of a Pod?
Answer: C
Explanation:
In Kubernetes, the standard way to execute a command inside a running container is kubectl exec, which is why A is correct. kubectl exec calls the Kubernetes API (API server), which then coordinates with the kubelet on the target node to run the requested command inside the container using the container runtime's exec mechanism. The -- separator is important: it tells kubectl that everything after -- is the command to run in the container rather than flags for kubectl itself.
This is fundamentally different from docker exec. In Kubernetes, you don't normally target containers through Docker/CRI tools directly because Kubernetes abstracts the runtime behind CRI. Also, "Docker" might not even be installed on nodes in modern clusters (containerd/CRI-O are common). So option B is not the Kubernetes-native approach and often won't work.
kubectl run (option C) is for creating a new Pod (or generating workload resources), not for executing a command in an existing container. kubectl attach (option D) attaches your terminal to a running container's process streams (stdin/stdout/stderr), which is useful for interactive sessions, but it does not execute an arbitrary new command like exec does.
In real usage, you often specify the container when a Pod has multiple containers: kubectl exec -it <pod> -c <container> -- /bin/sh. This is common for debugging, verifying config files mounted from ConfigMaps/Secrets, testing DNS resolution, or checking network connectivity from within the Pod network namespace. Because exec uses the API and kubelet, it respects Kubernetes access control (RBAC) and audit logging-another reason it's the correct operational method.
NEW QUESTION # 224
What is the core functionality of GitOps tools like Argo CD and Flux?
Answer: B
Explanation:
The defining capability of GitOps controllers such as Argo CD and Flux is continuous reconciliation: they compare the desired state stored in Git to the actual state in the cluster and then alert and/or correct drift, making D correct. In GitOps, Git becomes the single source of truth for declarative configuration (Kubernetes manifests, Helm charts, Kustomize overlays). The controller watches Git for changes and applies them, and it also watches the cluster for divergence.
This is more than "auditing human changes" (option A). GitOps does provide auditability because changes are made via commits and pull requests, but the core functionality is the reconciliation loop that keeps cluster state aligned with Git, including optional automated sync/remediation. Option B is not accurate because GitOps is not about tracking user Git commands; it's about reconciling desired state definitions. Option C (automatically creating pull requests for outdated dependencies) is a useful feature in some tooling ecosystems, but it is not the central defining behavior of GitOps controllers.
In Kubernetes delivery terms, this approach improves reliability: rollouts become repeatable, configuration drift is detected, and recovery is simpler (reapply known-good state from Git). It also supports separation of duties: platform teams can control policies and base layers, while app teams propose changes via PRs.
So the verified statement is: GitOps tools continuously reconcile Git desired state with cluster actual state
-exactly option D.
NEW QUESTION # 225
You are migrating a monolithic application to a microservices architecture on Kubernetes. You choose to use Istio to manage the communication between these new services. Which of the following is NOT a benefit of adopting Istio in this scenario?
Answer: B
Explanation:
While Istio provides benefits like centralized traffic management, security, and observability for microservices, it doesn't automatically simplify the process of migrating existing monolithic code. The migration itself requires careful refactoring and architectural changes, which Istio complements but doesn't replace. Options B, C, D, and E are all valid benefits of using Istio for microservices.
NEW QUESTION # 226
Kubernetes ___ allows you to automatically manage the number of nodes in your cluster to meet demand.
Answer: C
Explanation:
Kubernetes supports multiple autoscaling mechanisms, but they operate at different layers. The question asks specifically about automatically managing the number of nodes in the cluster, which is the role of the Cluster Autoscaler-therefore B is correct.
Cluster Autoscaler monitors the scheduling state of the cluster. When Pods are pending because there are not enough resources (CPU/memory) available on existing nodes-meaning the scheduler cannot place them-Cluster Autoscaler can request that the underlying infrastructure (typically a cloud provider node group / autoscaling group) add nodes. Conversely, when nodes are underutilized and Pods can be rescheduled elsewhere, Cluster Autoscaler can drain those nodes (respecting disruption constraints like PodDisruptionBudgets) and then remove them to reduce cost. This aligns with cloud-native elasticity: scale infrastructure up and down automatically based on workload needs.
The other options are different: Horizontal Pod Autoscaler (HPA) changes the number of Pod replicas for a workload (like a Deployment) based on metrics (CPU utilization, memory, or custom metrics). It scales the application layer, not the node layer. Vertical Pod Autoscaler (VPA) changes resource requests/limits (CPU/memory) for Pods, effectively "scaling up/down" the size of individual Pods. It also does not directly change node count, though its adjustments can influence scheduling pressure. "Node Autoscaler" is not the canonical Kubernetes component name used in standard terminology; the widely referenced upstream component for node count is Cluster Autoscaler.
In real systems, these autoscalers often work together: HPA increases replicas when traffic rises; that may cause Pods to go Pending if nodes are full; Cluster Autoscaler then adds nodes; scheduling proceeds; later, traffic drops, HPA reduces replicas and Cluster Autoscaler removes nodes. This layered approach provides both performance and cost efficiency.
NEW QUESTION # 227
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