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| Section | Objectives |
|---|---|
| Topic 1: Container Orchestration | - Container lifecycle and scheduling
|
| Topic 2: Cloud Native Application Delivery | - CI/CD and deployment strategies
|
| Topic 3: Cloud Security and Governance | - Security fundamentals in cloud native systems
|
| Topic 4: Cloud Native Architecture | - Microservices and distributed systems
|
| Topic 5: Cloud Native Observability | - Monitoring and logging
|
| Topic 6: Kubernetes Fundamentals | - Core Kubernetes concepts
|
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NEW QUESTION # 110
The Container Runtime Interface (CRI) defines the protocol for the communication between:
Answer: A
Explanation:
The CRI (Container Runtime Interface) defines how the kubelet talks to the container runtime, so A is correct. The kubelet is the node agent responsible for ensuring containers are running in Pods on that node. It needs a standardized way to request operations such as: create a Pod sandbox, pull an image, start/stop containers, execute commands, attach streams, and retrieve logs. CRI provides that contract so kubelet does not need runtime-specific integrations.
This interface is a key part of Kubernetes' modular design. Different container runtimes implement the CRI, allowing Kubernetes to run with containerd, CRI-O, and other CRI-compliant runtimes. This separation of concerns lets Kubernetes focus on orchestration, while runtimes focus on executing containers according to the OCI runtime spec, managing images, and handling low-level container lifecycle.
Why the other options are incorrect:
etcd is the control plane datastore; container runtimes do not communicate with etcd via CRI.
kube-apiserver and kubelet communicate using Kubernetes APIs, but CRI is not their protocol; CRI is specifically kubelet ↔ runtime.
container runtime and image registry communicate using registry protocols (image pull/push APIs), but that is not CRI. CRI may trigger image pulls via runtime requests, yet the actual registry communication is separate.
Operationally, this distinction matters when debugging node issues. If Pods are stuck in "ContainerCreating" due to image pull failures or runtime errors, you often investigate kubelet logs and the runtime (containerd/CRI-O) logs. Kubernetes administrators also care about CRI streaming (exec/attach/logs streaming), runtime configuration, and compatibility across Kubernetes versions.
So, the verified answer is A: the kubelet and the container runtime.
NEW QUESTION # 111
Which of the following is the correct command to run an nginx deployment with 2 replicas?
Answer: C
Explanation:
The correct answer is B: kubectl create deploy nginx --image=nginx --replicas=2. This uses kubectl create deployment (shorthand create deploy) to generate a Deployment resource named nginx with the specified container image. The --replicas=2 flag sets the desired replica count, so Kubernetes will create two Pod replicas (via a ReplicaSet) and keep that number stable.
Option A is incorrect because kubectl run is primarily intended to run a Pod (and in older versions could generate other resources, but it's not the recommended/consistent way to create a Deployment in modern kubectl usage). Option C is invalid syntax: kubectl subcommand order is incorrect; you don't say kubectl create nginx deployment. Option D uses a non-existent --count flag for Deployment replicas.
From a Kubernetes fundamentals perspective, this question tests two ideas: (1) Deployments are the standard controller for running stateless workloads with a desired number of replicas, and (2) kubectl create deployment is a common imperative shortcut for generating that resource. After running the command, you can confirm with kubectl get deploy nginx, kubectl get rs, and kubectl get pods -l app=nginx (label may vary depending on kubectl version). You'll see a ReplicaSet created and two Pods brought up.
In production, teams typically use declarative manifests (kubectl apply -f) or GitOps, but knowing the imperative command is useful for quick labs and validation. The key is that replicas are managed by the controller, not by manually starting containers-Kubernetes reconciles the state continuously.
Therefore, B is the verified correct command.
NEW QUESTION # 112
Let's assume that an organization needs to process large amounts of data in bursts, on a cloud-based Kubernetes cluster. For instance: each Monday morning, they need to run a batch of 1000 compute jobs of 1 hour each, and these jobs must be completed by Monday night. What's going to be the most cost-effective method?
Answer: A
Explanation:
Burst workloads are a classic elasticity problem: you need large capacity for a short window, then very little capacity the rest of the week. The most cost-effective approach in a cloud-based Kubernetes environment is to scale infrastructure dynamically, matching node count to current demand. That's exactly what Cluster Autoscaler is designed for: it adds nodes when Pods cannot be scheduled due to insufficient resources and removes nodes when they become underutilized and can be drained safely. Therefore B is correct.
Option A can work operationally, but it commonly results in paying for a reserved "standing army" of nodes that sit idle most of the week-wasteful for bursty patterns unless the nodes are repurposed for other workloads. Taints/tolerations and nodeSelectors are placement tools; they don't reduce cost by themselves and may increase waste if they isolate nodes. Option D (PriorityClasses) affects which Pods get scheduled first given available capacity, but it does not create capacity. If the cluster doesn't have enough nodes, high priority Pods will still remain Pending. Option C (reserved instances or committed-use discounts) can reduce unit price, but it assumes relatively predictable baseline usage. For true bursts, you usually want a smaller baseline plus autoscaling, and optionally combine it with discounted capacity types if your cloud supports them.
In Kubernetes terms, the control loop is: batch Jobs create Pods # scheduler tries to place Pods # if many Pods are Pending due to insufficient CPU/memory, Cluster Autoscaler observes this and increases the node group size # new nodes join and kube-scheduler places Pods # after jobs finish and nodes become empty, Cluster Autoscaler drains and removes nodes. This matches cloud-native principles: elasticity, pay-for-what-you-use, and automation. It minimizes idle capacity while still meeting the completion deadline.
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NEW QUESTION # 113
A Deployment named web-app is running in the production namespace with 2 replicas. The replica count must be increased to 5. Which kubectl command correctly scales the Deployment?
Answer: C
Explanation:
The kubectl scale command changes the desired replica count of an existing Deployment.
Specifying --replicas=5 updates the Deployment to maintain five Pods in the production namespace.
NEW QUESTION # 114
Consider the following Kubernetes YAML configuration for a Deployment:
What is the purpose of the 'replicas' field in this configuration, and how does it affect the Deployment? The 'replicas'' field specifies the number of Pods that should be created and maintained by the Deployment. It controls the desired number of running instances of the application.
Answer: A
Explanation:
The 'replicas' field in a Deployment specifies the number of Pods that should be created and maintained by the Deployment. It controls the desired number of running instances of the application. If a Pod fails, the Deployment will automatically create a new Pod to replace it, ensuring that the desired number of replicas is maintained.
NEW QUESTION # 115
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