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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Cloud Native Application Delivery | 8% | - Deployment Strategies
|
| Topic 2: Cloud Native Architecture | 16% | - Ecosystem & Landscape
|
| Topic 3: Container Orchestration | 22% | - Orchestration Principles
|
| Topic 4: Kubernetes Fundamentals | 46% | - Scheduling and Administration
|
| Topic 5: Cloud Native Observability | 8% | - Monitoring & Metrics
|
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NEW QUESTION # 101
Explain the difference between a Deployment and a StatefulSet in Kubernetes.
Answer: D,E
Explanation:
Deployments are used for deploying and managing stateless applications, while StatefulSets are used for deploying and managing stateful applications. Deployments are ideal for applications where the state of each Pod is not important, while StatefulSets are designed for applications that require persistent storage, unique network identities, and ordered scaling.
NEW QUESTION # 102
You're running a web application in Kubernetes that experiences occasional spikes in traffic. You want to scale your application horizontally to handle these spikes automatically. What Kubernetes resource should you utilize to achieve this?
Answer: D
Explanation:
A HonzontalPodAutoscaler (HPA) is the primary resource in Kubernetes for automatically scaling Pods based on resource utilization (CPU, memory, etc.) or custom metrics. Deployments, StatefulSets, and DaemonSets define the deployment and management of Pods, while Services handle load balancing and access to the application.
NEW QUESTION # 103
Can a Kubernetes Service expose multiple ports?
Answer: C
Explanation:
Yes, a Kubernetes Service can expose multiple ports, and when it does, each port should have a unique, unambiguous name, making B correct. In the Service spec, the ports field is an array, allowing you to define multiple port mappings (e.g., 80 for HTTP and 443 for HTTPS, or grpc and metrics). Each entry can include port (Service port), targetPort (backend Pod port), and protocol.
The naming requirement becomes important because Kubernetes needs to disambiguate ports, especially when other resources refer to them. For example, an Ingress backend or some proxies/controllers can reference Service ports by name. Also, when multiple ports exist, a name helps humans and automation reliably select the correct port. Kubernetes documentation and common practice recommend naming ports whenever there is more than one, and in several scenarios it's effectively required to avoid ambiguity.
Option A is incorrect because multi-port Services are common and fully supported. Option C is insufficient: while different port numbers are necessary, naming is the correct distinguishing rule emphasized by Kubernetes patterns and required by some integrations. Option D is incorrect and nonsensical-Services can expose many ports and are not restricted to 443.
Operationally, exposing multiple ports through one Service is useful when a single backend workload provides multiple interfaces (e.g., application traffic and a metrics endpoint). You can keep stable discovery under one DNS name while still differentiating ports. The backend Pods must still listen on the target ports, and selectors determine which Pods are endpoints. The key correctness point for this question is: multi-port Services are allowed, and each port should be uniquely named to avoid confusion and integration issues.
NEW QUESTION # 104
You are managing a large Kubernetes cluster with multiple namespaces. You want to control access to resources within different namespaces. Which of the following mechanisms can be used to achieve fine-grained access control?
Answer: A,E
Explanation:
Both Role-Based Access Control (RBAC) and Pod Security Policies (PSP) are used for managing access to resources within Kubernetes. RBAC provides fine-grained permissions based on roles and users, while PSPs define security constraints for Pods, limiting their capabilities and access to resources.
NEW QUESTION # 105
In which framework do the developers no longer have to deal with capacity, deployments, scaling and fault tolerance, and OS?
Answer: B
Explanation:
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.
NEW QUESTION # 106
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