Kubernetes components work together to ensure the availability and proper functioning of resources

like ReplicaSets. Let’s analyze each option:

A . kube-proxy

Incorrect: The kube-proxy manages network communication for services and pods by implementing

load balancing and routing rules. It does not guarantee the availability of ReplicaSet pods.

B . kube-scheduler

Incorrect: The kube-scheduler is responsible for assigning pods to nodes based on resource

availability and other constraints. While it plays a role in pod placement, it does not ensure the

availability of ReplicaSet pods.

C . kube controller

Correct: The kube controller (specifically the ReplicaSet controller) ensures that the desired number

of pods specified in a ReplicaSet are running at all times. If a pod crashes or is deleted, the controller

creates a new one to maintain the desired state.

D . kubelet

Incorrect: The kubelet ensures that containers are running as expected on a node but does not

manage the overall availability of ReplicaSet pods across the cluster.

Why Kube Controller?

ReplicaSet Management: The ReplicaSet controller within the kube controller manager ensures that

the specified number of pod replicas are always available.

Self-Healing: If a pod fails or is deleted, the controller automatically creates a new pod to maintain

the desired state.

JNCIA Cloud Reference:

The JNCIA-Cloud certification covers Kubernetes control plane components, including the kube

controller. Understanding the role of the kube controller is essential for managing the availability and

scalability of Kubernetes resources.

For example, Juniper Contrail integrates with Kubernetes to provide advanced networking and

security features, relying on the kube controller to maintain the desired state of ReplicaSets.

Reference:

Kubernetes Documentation: ReplicaSet Controller

Juniper JNCIA-Cloud Study Guide: Kubernetes Control Plane

Docker is a popular containerization platform that relies on a container runtime to manage the

lifecycle of containers. The container runtime is responsible for tasks such as creating, starting,

stopping, and managing containers. Let’s analyze each option:

A . docker_cli

Incorrect: The Docker CLI (Command Line Interface) is a tool used to interact with the Docker

daemon (dockerd). It is not a container runtime but rather a user interface for managing Docker

containers.

B . containerd

Correct: containerd is the default container runtime used by Docker. It is a lightweight, industry-

standard runtime that handles low-level container management tasks, such as image transfer,

container execution, and lifecycle management. Docker delegates these tasks to containerd through

the Docker daemon.

C . dockerd

Incorrect: dockerd is the Docker daemon, which manages Docker objects such as images, containers,

networks, and volumes. While dockerd interacts with the container runtime, it is not the runtime

itself.

D . cri-o

Incorrect: cri-o is an alternative container runtime designed specifically for Kubernetes. It

implements the Kubernetes Container Runtime Interface (CRI) and is not used by Docker.

Why containerd?

Industry Standard: containerd is a widely adopted container runtime that adheres to the Open

Container Initiative (OCI) standards.

Integration with Docker: Docker uses containerd as its default runtime, making it the correct answer

in this context.

JNCIA Cloud Reference:

The JNCIA-Cloud certification emphasizes understanding containerization technologies and their

components. Docker and its runtime (containerd) are foundational tools in modern cloud

environments, enabling lightweight, portable, and scalable application deployment.

For example, Juniper Contrail integrates with container orchestration platforms like Kubernetes,

which often use containerd as the underlying runtime. Understanding container runtimes is essential

for managing containerized workloads in cloud environments.

Reference:

Docker Documentation: Container Runtimes

Open Container Initiative (OCI) Standards

Juniper JNCIA-Cloud Study Guide: Containerization

Kubernetes combined with KubeVirt enables the hosting of virtual machines (VMs) alongside

containerized workloads. This setup aligns with a specific cloud service model. Let’s analyze each

option:

A . Software as a Service (SaaS)

Incorrect: SaaS delivers fully functional applications over the internet, such as Salesforce or Google

Workspace. Hosting VMs using Kubernetes and KubeVirt does not fall under this category.

B . Platform as a Service (PaaS)

Incorrect: PaaS provides a platform for developers to build, deploy, and manage applications without

worrying about the underlying infrastructure. While Kubernetes itself can be considered a PaaS

component, hosting VMs goes beyond this model.

C . Infrastructure as a Service (IaaS)

Correct: IaaS provides virtualized computing resources such as servers, storage, and networking over

the internet. By hosting VMs using Kubernetes and KubeVirt, you are offering infrastructure-level

services, which aligns with the IaaS model.

D . Bare Metal as a Service (BMaaS)

Incorrect: BMaaS provides direct access to physical servers without virtualization. Kubernetes and

KubeVirt focus on virtualized environments, making this option incorrect.

Why IaaS?

Virtualized Resources: Hosting VMs using Kubernetes and KubeVirt provides virtualized

infrastructure, which is the hallmark of IaaS.

Scalability and Flexibility: Users can provision and manage VMs on-demand, similar to traditional

IaaS offerings like AWS EC2 or OpenStack.

JNCIA Cloud Reference:

The JNCIA-Cloud certification emphasizes understanding cloud service models, including IaaS.

Recognizing how Kubernetes and KubeVirt fit into the IaaS paradigm is essential for designing hybrid

cloud solutions.

For example, Juniper Contrail integrates with Kubernetes and KubeVirt to provide advanced

networking and security features for IaaS-like environments.

Reference:

KubeVirt Documentation

Juniper JNCIA-Cloud Study Guide: Cloud Service Models

Linux provides several features to manage system resources and isolate processes. Let’s analyze each

option:

A . virtual routing and forwarding instances

Incorrect: Virtual Routing and Forwarding (VRF) is a networking feature used to create multiple

routing tables on a single router or host. It is unrelated to limiting memory, CPU, or network

utilization for processes.

B . network namespaces

Incorrect: Network namespaces are used to isolate network resources (e.g., interfaces, routing

tables) for processes. While they can help with network isolation, they do not directly limit memory

or CPU usage.

C . control groups

Correct: Control Groups (cgroups) are a Linux kernel feature that allows you to limit, account for, and

isolate the resource usage (CPU, memory, disk I/O, network) of a set of processes. cgroups are

commonly used in containerization technologies like Docker and Kubernetes to enforce resource

limits.

D . slicing

Incorrect: "Slicing" is not a recognized Linux feature for resource management. This term may refer

to dividing resources in other contexts but is not relevant here.

Why Control Groups?

Resource Management: cgroups provide fine-grained control over memory, CPU, and network

utilization, ensuring that processes do not exceed their allocated resources.

Containerization Foundation: cgroups are a core technology behind container runtimes like

containerd and orchestration platforms like Kubernetes.

JNCIA Cloud Reference:

The JNCIA-Cloud certification covers Linux features like cgroups as part of its containerization

curriculum. Understanding cgroups is essential for managing resource allocation in cloud

environments.

For example, Juniper Contrail integrates with Kubernetes to manage containerized workloads,

leveraging cgroups to enforce resource limits.

Reference:

Linux Kernel Documentation: Control Groups

Juniper JNCIA-Cloud Study Guide: Linux Features

OpenShift provides multiple methods for installing and deploying clusters, depending on the level of

control and automation desired. Let’s analyze each option:

A . installer-provisioned infrastructure

Correct:

Installer-provisioned infrastructure (IPI) is an automated installation method where the OpenShift

installer provisions and configures the underlying infrastructure (e.g., virtual machines, networking)

using cloud provider APIs or bare-metal platforms. This method simplifies deployment by handling

most of the setup automatically.

B . kubeadm

Incorrect:

kubeadm is a tool used to bootstrap Kubernetes clusters manually. While it is widely used for

Kubernetes installations, it is not specific to OpenShift and is not an official installation method for

OpenShift clusters.

C . user-provisioned infrastructure

Correct:

User-provisioned infrastructure (UPI) is a manual installation method where users prepare and

configure the infrastructure (e.g., virtual machines, load balancers, DNS) before deploying OpenShift.

This method provides greater flexibility and control over the environment but requires more effort

from the user.

D . kubespray

Incorrect:

Kubespray is an open-source tool used to deploy Kubernetes clusters on various infrastructures. Like

kubeadm, it is not specific to OpenShift and is not an official installation method for OpenShift

clusters.

Why These Methods?

Installer-Provisioned Infrastructure (IPI): Automates the entire installation process, making it ideal for

users who want a quick and hassle-free deployment.

User-Provisioned Infrastructure (UPI): Allows advanced users to customize the infrastructure and

tailor the deployment to their specific needs.

JNCIA Cloud Reference:

The JNCIA-Cloud certification covers OpenShift installation methods as part of its curriculum on

container orchestration platforms. Understanding the differences between IPI and UPI is essential for

deploying OpenShift clusters effectively.

For example, Juniper Contrail integrates with OpenShift to provide advanced networking features,

regardless of whether the cluster is deployed using IPI or UPI.

Reference:

OpenShift Documentation: Installation Methods

Juniper JNCIA-Cloud Study Guide: OpenShift Deployment