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| Section | Weight | Objectives |
|---|---|---|
| Container Orchestration | 22% | - Networking
|
| Kubernetes Fundamentals | 46% | - Kubernetes Resources
|
| Cloud Native Observability | 8% | - Logging
|
| Cloud Native Application Delivery | 8% | - GitOps
|
| Cloud Native Architecture | 16% | - Architecture Concepts
|
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NEW QUESTION # 160
Which component of the Kubernetes architecture is responsible for integration with the CRI container runtime?
Answer: B
Explanation:
The correct answer is B: kubelet. The Container Runtime Interface (CRI) defines how Kubernetes interacts with container runtimes in a consistent, pluggable way. The component that speaks CRI is the kubelet, the node agent responsible for running Pods on each node. When the kube-scheduler assigns a Pod to a node, the kubelet reads the PodSpec and makes the runtime calls needed to realize that desired state-pull images, create a Pod sandbox, start containers, stop containers, and retrieve status and logs. Those calls are made via CRI to a CRI-compliant runtime such as containerd or CRI-O.
Why not the others:
kubeadm bootstraps clusters (init/join/upgrade workflows) but does not run containers or speak CRI for workload execution.
kube-apiserver is the control plane API frontend; it stores and serves cluster state and does not directly integrate with runtimes.
kubectl is just a client tool that sends API requests; it is not involved in runtime integration on nodes.
This distinction matters operationally. If the runtime is misconfigured or CRI endpoints are unreachable, kubelet will report errors and Pods can get stuck in ContainerCreating, image pull failures, or runtime errors. Debugging often involves checking kubelet logs and runtime service health, because kubelet is the integration point bridging Kubernetes scheduling/state with actual container execution.
So, the node-level component responsible for CRI integration is the kubelet-option B.
NEW QUESTION # 161
What is the main purpose of a DaemonSet?
Answer: C
Explanation:
The correct answer is A. A DaemonSet is a workload controller whose job is to ensure that a specific Pod runs on all nodes (or on a selected subset of nodes) in the cluster. This is fundamentally different from Deployments/ReplicaSets, which aim to maintain a certain replica count regardless of node count. With a DaemonSet, the number of Pods is implicitly tied to the number of eligible nodes: add a node, and the DaemonSet automatically schedules a Pod there; remove a node, and its Pod goes away.
DaemonSets are commonly used for node-level services and background agents: log collectors, node monitoring agents, storage daemons, CNI components, or security agents-anything where you want a presence on each node to interact with node resources. This aligns with option D's phrasing ("agent on every node"), but option A is the canonical definition and is slightly broader because it covers "all or certain nodes" (via node selectors/affinity/taints-tolerations) and the fact that the unit is a Pod.
Why the other options are wrong: DaemonSets do not "keep kubelet running" (B); kubelet is a node service managed by the OS. DaemonSets do not use a replicas field to maintain a specific count (C); that's Deployment/ReplicaSet behavior.
Operationally, DaemonSets matter for cluster operations because they provide consistent node coverage and automatically react to node pool scaling. They also require careful scheduling constraints so they land only where intended (e.g., only Linux nodes, only GPU nodes). But the main purpose remains: ensure a copy of a Pod runs on each relevant node-option A.
NEW QUESTION # 162
What are the most important resources to guarantee the performance of an etcd cluster?
Answer: A
NEW QUESTION # 163
You are running a stateful application on Kubernetes that requires persistent data storage. Which of the following Kubernetes storage classes would be most suitable for this scenario?
Answer: E
Explanation:
Persistent Volumes (PVs) are the most suitable storage class for stateful applications- They guarantee persistent data storage across pod restarts and provide a consistent interface for accessing the data
NEW QUESTION # 164
Which Kubernetes feature would you use to guard against split brain scenarios with your distributed application?
Answer: A
Explanation:
The exam-expected Kubernetes feature here is StatefulSet, so D is the correct answer. StatefulSets are designed for distributed/stateful applications that require stable network identities, stable storage, and ordered deployment/termination. Those properties are commonly required by systems that must avoid
"split brain" behaviors-where multiple nodes believe they are the leader/primary due to partitions or identity confusion.
StatefulSets give each Pod a persistent identity (e.g., app-0, app-1) and stable DNS naming (typically via a headless Service), which supports consistent peer discovery and membership. They also commonly pair with PersistentVolumeClaims so that each replica keeps its own data across restarts and reschedules. The ordered rollout semantics help clustered systems bootstrap and expand in controlled sequences, reducing the chance of chaotic membership changes.
Important nuance: StatefulSet alone does not magically prevent split brain. Split brain prevention is primarily a property of the application's own clustering/consensus design (e.g., leader election, quorum, fencing). That's why option B ("consensus protocols") is conceptually the true prevention mechanism-but it' s not a Kubernetes feature in the way the question frames it. Kubernetes provides primitives that make it feasible to run such systems safely (stable IDs, stable storage, predictable DNS), and StatefulSet is the Kubernetes workload API designed for that class of distributed stateful apps.
Replication controllers and rolling updates don't address identity/quorum concerns. Therefore, within Kubernetes constructs, StatefulSet is the best verified choice for workloads needing stable identity patterns commonly used to reduce split-brain risk.
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NEW QUESTION # 165
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