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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Container Orchestration | 22% | - Networking & Storage
|
| Topic 2: Cloud Native Application Delivery | 8% | - Deployment Strategies
|
| Topic 3: Kubernetes Fundamentals | 46% | - Kubernetes Core Concepts
|
| Topic 4: Cloud Native Architecture | 16% | - Ecosystem & Landscape
|
| Topic 5: Cloud Native Observability | 8% | - Monitoring & Metrics
|
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NEW QUESTION # 160
Which of the following is a valid PromQL query?
Answer: B
Explanation:
Prometheus Query Language (PromQL) uses a function-and-selector syntax, not SQL. A valid query typically starts with a metric name and optionally includes label matchers in curly braces. In the simplified quiz syntax given, the valid PromQL-style selector is best represented by D: http_requests_total(job="apiserver"), so D is correct.
Conceptually, what this query means is "select time series for the metric http_requests_total where the job label equals apiserver." In standard PromQL formatting you most often see this as: http_requests_total{job="apiserver"}. Many training questions abbreviate braces and focus on the idea of filtering by labels; the key is that PromQL uses label matchers rather than SQL WHERE clauses.
Options A and C are invalid because they use SQL (SELECT * FROM ...) which is not PromQL. Option B is also invalid because PromQL does not use the keyword WHERE. PromQL filtering is done by applying label matchers directly to the metric selector.
In Kubernetes observability, PromQL is central to building dashboards and alerts from cluster metrics. For example, you might compute rates from counters: rate(http_requests_total{job="apiserver"}[5m]), aggregate by labels: sum by (code) (...), or alert on error ratios. Understanding the selector and label-matcher model is foundational because Prometheus metrics are multi-dimensional-labels define the slices you can filter and aggregate on.
So, within the provided options, D is the only one that follows PromQL's metric+label-filter style and therefore is the verified correct answer.
NEW QUESTION # 161
What component enables end users, different parts of the Kubernetes cluster, and external components to communicate with one another?
Answer: A
Explanation:
The Kubernetes API is the central interface that enables communication between users, controllers, nodes, and external integrations, so C is correct. Kubernetes is fundamentally an API-driven system: all cluster state is represented as API objects, and all operations-create, update, delete, watch-flow through the API server.
End users typically interact with the Kubernetes API using tools like kubectl, client libraries, or dashboards.
But those tools are clients; the shared communication "hub" is the API itself. Inside the cluster, core control plane components (controllers, scheduler) continuously watch the API for desired state and write status updates back. Worker nodes (via kubelet) also communicate with the API server to receive Pod specs, report node health, and update Pod statuses. External systems-cloud provider integrations, CI/CD pipelines, GitOps controllers, monitoring and policy engines-also integrate primarily through the Kubernetes API.
Option A (kubectl) is a CLI that talks to the Kubernetes API; it is not the underlying component that all parts use to communicate. Options B and D are cloud-provider tools and are not universal to Kubernetes clusters.
Kubernetes runs across many environments, and the consistent interoperability layer is the Kubernetes API.
This API-centric architecture is what enables Kubernetes' declarative model: you submit desired state to the API, and controllers reconcile actual state to match. It also enables extensibility: CRDs and admission webhooks expand what the API can represent and enforce. Therefore, the correct answer is C: Kubernetes API.
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NEW QUESTION # 162
Which of the following options includes valid API versions?
Answer: B
Explanation:
Kubernetes API versions follow a consistent naming pattern that indicates stability level and versioning. The valid forms include stable versions like v1, and pre-release versions such as v1alpha1, v1beta1, etc. Option C contains valid-looking Kubernetes version strings-v1alpha1, v2beta3, v2-so C is correct.
In Kubernetes, the "v" prefix is part of the standard for API versions. A stable API uses v1, v2, etc. Pre- release APIs include a stability marker: alpha (earliest, most changeable) and beta (more stable but still may change). The numeric suffix (e.g., alpha1, beta3) indicates iteration within that stability stage.
Option A is invalid because strings like alpha1v1 and beta3v3 do not match Kubernetes conventions (the v comes first, and alpha/beta are qualifiers after the version: v1alpha1). Option B is invalid because alpha1 and beta3 are missing the leading version prefix; Kubernetes API versions are not just "alpha1." Option D includes 2.0, which looks like semantic versioning but is not the Kubernetes API version format. Kubernetes uses v2, not 2.0, for API versions.
Understanding this matters because API versions signal compatibility guarantees. Stable APIs are supported for a defined deprecation window, while alpha/beta APIs may change in incompatible ways and can be removed more easily. When authoring manifests, selecting the correct apiVersion ensures the API server accepts your resource and that controllers interpret fields correctly.
Therefore, among the choices, C is the only option comprised of valid Kubernetes-style API version strings.
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NEW QUESTION # 163
Which key-value store is used to persist Kubernetes cluster data?
Answer: C
Explanation:
Kubernetes stores its cluster state (API objects) in etcd, making A correct. etcd is a distributed, strongly consistent key-value store that serves as the source of truth for the Kubernetes control plane. When you create or update objects such as Pods, Deployments, ConfigMaps, Secrets, or Nodes, the kube-apiserver validates the request and then persists the desired state into etcd. Controllers and the scheduler watch the API for changes (which ultimately reflect etcd state) and reconcile the cluster to match that desired state.
etcd's consistency guarantees are crucial. Kubernetes relies on accurate, up-to-date state to make scheduling decisions, enforce RBAC/admission policies, coordinate leader elections, and ensure controllers behave correctly. etcd uses the Raft consensus algorithm to replicate data among members and requires quorum for writes, enabling fault tolerance when deployed in HA configurations (commonly three or five members).
The other options are incorrect in Kubernetes' standard architecture. ZooKeeper is a distributed coordination system used by some other platforms, but Kubernetes does not use it as its primary datastore. Redis is an in-memory data store used for caching or messaging, not as Kubernetes' authoritative state store. "ControlPlaneStore" is not a standard Kubernetes component.
Operationally, etcd health is one of the most important determinants of cluster reliability. Slow disk I/O or unstable networking can degrade etcd performance and cause API latency spikes. Backup and restore procedures for etcd are critical disaster-recovery practices, and securing etcd (TLS, access restrictions) is essential because it may contain sensitive data (e.g., Secrets-often base64-encoded, and optionally encrypted at rest depending on configuration).
Therefore, the verified Kubernetes datastore is etcd, option A.
NEW QUESTION # 164
Which of the following resources helps in managing a stateless application workload on a Kubernetes cluster?
Answer: A
Explanation:
The correct answer is D: Deployment. A Deployment is the standard Kubernetes controller for managing stateless applications. It provides declarative updates, replica management, and rollout/rollback functionality. You define the desired state (container image, environment variables, ports, replica count) in the Deployment spec, and Kubernetes ensures the specified number of Pods are running and updated according to strategy (RollingUpdate by default).
Stateless workloads are ideal for Deployments because each replica is interchangeable. If a Pod dies, a new one can be created anywhere; if traffic increases, replicas can be increased; if you need to update the app, a new ReplicaSet is created and traffic shifts gradually to new Pods. Deployments integrate naturally with Services for stable networking and load balancing.
Why the other options are incorrect:
A DaemonSet ensures one Pod per node (or selected nodes). It's for node-level agents, not generic stateless service replicas.
A StatefulSet is for workloads needing stable identity, ordered rollout, and persistent storage per replica (databases, quorum systems). That's not the typical stateless app case.
kubectl is a CLI tool; it doesn't "manage" workloads as a controller resource.
In real cluster operations, almost every stateless microservice is represented as a Deployment plus a Service (and often an Ingress/Gateway for edge routing). Deployments also support advanced delivery patterns (maxSurge/maxUnavailable tuning) and easy integration with HPA for horizontal scaling. Because the question is specifically "managing a stateless application workload," the Kubernetes resource designed for that is clearly the Deployment.
NEW QUESTION # 165
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