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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Container Orchestration | 22% | - Networking & Storage
|
| Topic 2: Cloud Native Architecture | 16% | - Cloud Native Principles
|
| Topic 3: Cloud Native Observability | 8% | - Logging & Tracing
|
| Topic 4: Kubernetes Fundamentals | 46% | - Kubernetes Core Concepts
|
| Topic 5: Cloud Native Application Delivery | 8% | - Delivery Models
|
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32. Frage
What is the correct hierarchy of Kubernetes components?
Antwort: B
Begründung:
The correct answer is C: Cluster → Nodes → Pods → Containers. This expresses the fundamental structural relationship in Kubernetes. A cluster is the overall system (control plane + nodes) that runs your workloads. Inside the cluster, you have nodes (worker machines-VMs or bare metal) that provide CPU, memory, storage, and networking. The scheduler assigns workloads to nodes.
Workloads are executed as Pods, which are the smallest deployable units Kubernetes schedules. Pods represent one or more containers that share networking (one Pod IP and port space) and can share storage volumes. Within each Pod are containers, which are the actual application processes packaged with their filesystem and runtime dependencies.
The other options are incorrect because they break these containment relationships. Containers do not contain Pods; Pods contain containers. Nodes do not exist "inside" Pods; Pods run on nodes. And the cluster is the top-level boundary that contains nodes and orchestrates Pods.
This hierarchy matters for troubleshooting and design. If you're thinking about capacity, you reason at the node and cluster level (node pools, autoscaling, quotas). If you're thinking about application scaling, you reason at the Pod level (replicas, HPA, readiness probes). If you're thinking about process-level concerns, you reason at the container level (images, security context, runtime user, resources). Kubernetes intentionally uses this layered model so that scheduling and orchestration operate on Pods, while the container runtime handles container execution details.
So the accurate hierarchy from largest to smallest unit is: Cluster → Nodes → Pods → Containers, which corresponds to C.
33. Frage
What CNCF project is the leading DNS project in the CNCF landscape?
Antwort: B
Begründung:
https://github.com/cncf/landscape#trail-map
34. Frage
In which framework do the developers no longer have to deal with capacity, deployments, scaling and fault tolerance, and OS?
Antwort: B
Begründung:
Serverless is the model where developers most directly avoid managing server capacity, OS operations, and much of the deployment/scaling/fault-tolerance mechanics, which is why D is correct. In serverless computing (commonly Function-as-a-Service, FaaS, and managed serverless container platforms), the provider abstracts away the underlying servers. You typically deploy code (functions) or a container image, define triggers (HTTP events, queues, schedules), and the platform automatically provisions the required compute, scales it based on demand, and handles much of the availability and fault tolerance behind the scenes.
It's important to compare this to Kubernetes: Kubernetes does automate scheduling, self-healing, rolling updates, and scaling, but it still requires you (or your platform team) to design and operate cluster capacity, node pools, upgrades, runtime configuration, networking, and baseline reliability controls. Even in managed Kubernetes services, you still choose node sizes, scale policies, and operational configuration. Kubernetes reduces toil, but it does not eliminate infrastructure concerns in the same way serverless does.
Docker Swarm and Mesos are orchestration platforms that schedule workloads, but they also require managing the underlying capacity and OS-level aspects. They are not "no longer have to deal with capacity and OS" frameworks.
From a cloud native viewpoint, serverless is about consuming compute as an on-demand utility. Kubernetes can be a foundation for a serverless experience (for example, with event-driven autoscaling or serverless frameworks), but the pure framework that removes the most operational burden from developers is serverless.
35. Frage
In the Kubernetes platform, which component is responsible for running containers?
Antwort: C
Begründung:
In Kubernetes, the actual act of running containers on a node is performed by the container runtime. The kubelet instructs the runtime via CRI, and the runtime pulls images, creates containers, and manages their lifecycle. Among the options provided, CRI-O is the only container runtime, so B is correct.
It's important to be precise: the component that "runs containers" is not the control plane and not etcd. etcd (option A) stores cluster state (API objects) as the backing datastore. It never runs containers. cloud- controller-manager (option C) integrates with cloud APIs for infrastructure like load balancers and nodes.
kube-controller-manager (option D) runs controllers that reconcile Kubernetes objects (Deployments, Jobs, Nodes, etc.) but does not execute containers on worker nodes.
CRI-O is a CRI implementation that is optimized for Kubernetes and typically uses an OCI runtime (like runc) under the hood to start containers. Another widely used runtime is containerd. The runtime is installed on nodes and is a prerequisite for kubelet to start Pods. When a Pod is scheduled to a node, kubelet reads the PodSpec and asks the runtime to create a "pod sandbox" and then start the container processes. Runtime behavior also includes pulling images, setting up namespaces/cgroups, and exposing logs/stdout streams back to Kubernetes tooling.
So while "the container runtime" is the most general answer, the question's option list makes CRI-O the correct selection because it is a container runtime responsible for running containers in Kubernetes.
=========
36. Frage
Which Kubernetes Service type exposes a service only within the cluster?
Antwort: D
Begründung:
In Kubernetes, a Service provides a stable network endpoint for a set of Pods and abstracts away their dynamic nature. Kubernetes offers several Service types, each designed for different exposure requirements.
Among these, ClusterIP is the Service type that exposes an application only within the cluster, making it the correct answer.
When a Service is created with the ClusterIP type, Kubernetes assigns it a virtual IP address that is reachable exclusively from within the cluster's network. This IP is used by other Pods and internal components to communicate with the Service through cluster DNS or environment variables. External traffic from outside the cluster cannot directly access a ClusterIP Service, which makes it ideal for internal APIs, backend services, and microservices that should not be publicly exposed.
Option B (NodePort) is incorrect because NodePort exposes the Service on a static port on each node's IP address, allowing access from outside the cluster. Option C (LoadBalancer) is incorrect because it provisions an external load balancer-typically through a cloud provider-to expose the Service publicly. Option D (ExternalName) is incorrect because it does not create a proxy or internal endpoint at all; instead, it maps the Service name to an external DNS name outside the cluster.
ClusterIP is also the default Service type in Kubernetes. If no type is explicitly specified in a Service manifest, Kubernetes automatically assigns it as ClusterIP. This default behavior reflects the principle of least exposure, encouraging internal-only access unless external access is explicitly required.
From a cloud native architecture perspective, ClusterIP Services are fundamental to building secure, scalable microservices systems. They enable internal service-to-service communication while reducing the attack surface by preventing unintended external access.
According to Kubernetes documentation, ClusterIP Services are intended for internal communication within the cluster and are not reachable from outside the cluster network. Therefore, ClusterIP is the correct and fully verified answer, making option A the right choice.
37. Frage
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