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| Section | Weight | Objectives |
|---|---|---|
| Cloud Native Application Delivery | 8% | - CI/CD
|
| Kubernetes Fundamentals | 46% | - Kubernetes Architecture
|
| Cloud Native Architecture | 16% | - Architecture Concepts
|
| Container Orchestration | 22% | - Service Mesh
|
| Cloud Native Observability | 8% | - Logging
|
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NEW QUESTION # 311
What is the goal of load balancing?
Answer: A
Explanation:
The core goal of load balancing is to distribute incoming requests across multiple instances of a service so that no single instance becomes overloaded and so that the overall service is more available and responsive.
That matches option D, which is the correct answer.
In Kubernetes, load balancing commonly appears through the Service abstraction. A Service selects a set of Pods using labels and provides stable access via a virtual IP (ClusterIP) and DNS name. Traffic sent to the Service is then forwarded to one of the healthy backend Pods. This spreads load across replicas and provides resilience: if one Pod fails, it is removed from endpoints (or becomes NotReady) and traffic shifts to remaining replicas. The actual traffic distribution mechanism depends on the networking implementation (kube-proxy using iptables/IPVS or an eBPF dataplane), but the intent remains consistent: distribute requests across multiple backends.
Option A describes monitoring/observability, not load balancing. Option B describes progressive delivery patterns like canary or A/B routing; that can be implemented with advanced routing layers (Ingress controllers, service meshes), but it's not the general definition of load balancing. Option C describes scheduling/placement of instances (Pods) across cluster nodes, which is the role of the scheduler and controllers, not load balancing.
In cloud environments, load balancing may also be implemented by external load balancers (cloud LBs) in front of the cluster, then forwarded to NodePorts or ingress endpoints, and again balanced internally to Pods.
At each layer, the objective is the same: spread request traffic across multiple service instances to improve performance and availability.
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NEW QUESTION # 312
Which of the following options includes valid API versions?
Answer: D
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 # 313
You are building a CI/CD pipeline to deploy a Java application to Kubernetes. The application requires a specific Java version and some libraries to run correctly. How would you ensure that the correct runtime environment is available in the Kubernetes cluster?
Answer: A,B
Explanation:
The most common approaches are to include the required Java and libraries within the Dockerfile to create a self-contained image, or to use Kubernetes Init Containers. Init containers run before the main application container, installing the dependencies, ensuring the proper runtime environment is available.
NEW QUESTION # 314
What native runtime is Open Container Initiative (OCI) compliant?
Answer: D
Explanation:
The Open Container Initiative (OCI) publishes open specifications for container images and container runtimes so that tools across the ecosystem remain interoperable. When a runtime is "OCI-compliant," it means it implements the OCI Runtime Specification (how to run a container from a filesystem bundle and configuration) and/or works cleanly with OCI image formats through the usual layers (image โ unpack โ runtime). runC is the best-known, widely used reference implementation of the OCI runtime specification and is the low-level runtime underneath many higher-level systems. In Kubernetes, you typically interact with a higher-level container runtime (such as containerd or CRI-O) through the Container Runtime Interface (CRI). That higher-level runtime then uses a low-level OCI runtime to actually create Linux namespaces/cgroups, set up the container process, and start it. In many default installations, containerd delegates to runC for this low-level "create/start" work.
The other options are related but differ in what they are: Kata Containers uses lightweight VMs to provide stronger isolation while still presenting a container-like workflow; gVisor provides a user-space kernel for sandboxing containers; these can be used with Kubernetes via compatible integrations, but the canonical "native OCI runtime" answer in most curricula is runC. Finally, "runV" is not a common modern Kubernetes runtime choice in typical OCI discussions. So the most correct, standards-based answer here is A (runC) because it directly implements the OCI runtime spec and is commonly used as the default low-level runtime behind CRI implementations.
NEW QUESTION # 315
What is the primary mechanism to identify grouped objects in a Kubernetes cluster?
Answer: C
Explanation:
Kubernetes groups and organizes objects primarily using labels, so B is correct. Labels are key-value pairs attached to objects (Pods, Deployments, Services, Nodes, etc.) and are intended to be used for identifying, selecting, and grouping resources in a flexible, user-defined way.
Labels enable many core Kubernetes behaviors. For example, a Service selects the Pods that should receive traffic by matching a label selector against Pod labels. A Deployment's ReplicaSet similarly uses label selectors to determine which Pods belong to the replica set. Operators and platform tooling also rely on labels to group resources by application, environment, team, or cost center. This is why labeling is considered foundational Kubernetes hygiene: consistent labels make automation, troubleshooting, and governance easier.
A "label selector" (option C) is how you query/group objects based on labels, but the underlying primary mechanism is still the labels themselves. Without labels applied to objects, selectors have nothing to match. Custom Resources (option A) extend the API with new kinds, but they are not the primary grouping mechanism across the cluster. "Pod" (option D) is a workload unit, not a grouping mechanism.
Practically, Kubernetes recommends common label keys like app.kubernetes.io/name, app.kubernetes.io/instance, and app.kubernetes.io/part-of to standardize grouping. Those conventions improve interoperability with dashboards, GitOps tooling, and policy engines.
So, when the question asks for the primary mechanism used to identify grouped objects in Kubernetes, the most accurate answer is Labels (B)-they are the universal metadata primitive used to group and select resources.
NEW QUESTION # 316
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