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試験の準備方法-素晴らしいKCNAブロンズ教材試験-ハイパスレートのKCNA認証資格

さらに、MogiExam KCNAダンプの一部が現在無料で提供されています：https://drive.google.com/open?id=1SDNykfU0p3X-08GDxcRrwoGM12nokAYn

MogiExamのLinux FoundationのKCNA試験トレーニング資料は必要とするすべての人に成功をもたらすことができます。Linux FoundationのKCNA試験は挑戦がある認定試験です。現在、書籍の以外にインターネットは知識の宝庫として見られています。MogiExam で、あなたにあなたの宝庫を見つけられます。MogiExam はLinux FoundationのKCNA試験に関連する知識が全部含まれていますから、あなたにとって難しい問題を全て解決して差し上げます。

Linux Foundation KCNA（Kubernetes and Cloud Native Associate）試験は、Kubernetesを使用してクラウドネイティブアプリケーションを展開および管理するITプロフェッショナルの能力をテストする認定プログラムです。 Kubernetesは、コンテナ化されたアプリケーションの展開、スケーリング、管理を自動化する能力により、世界中の組織でますます人気が高まっています。その結果、Kubernetesスキルを持つプロフェッショナルの需要が急速に増加しており、KCNA認定はクラウドネイティブスペースでキャリアアップを目指す人にとって重要な資格となっています。

>> KCNAブロンズ教材 <<

一番優秀KCNA｜有効的なKCNAブロンズ教材試験｜試験の準備方法Kubernetes and Cloud Native Associate認証資格

一般的には、IT技術会社ではLinux Foundation KCNA資格認定を持つ職員の給料は持たない職員の給料に比べ、15％より高いです。これなので、IT技術職員としてのあなたはMogiExamのLinux Foundation KCNA問題集デモを参考し、試験の準備に速く行動しましょう。我々社はあなたがLinux Foundation KCNA試験に一発的に合格するために、最新版の備考資料を提供します。

Linux Foundation Kubernetes and Cloud Native Associate 認定 KCNA 試験問題 (Q81-Q86):

質問 # 81
You are developing a serverless application using Azure Functions that processes real-time streaming dat
a. How would you ensure reliable and efficient data ingestion from a Kafka topic to your Azure Functions?

A. Use Azure Service Bus to act as a message broker between Kafka and Azure Functions.
B. Configure Azure Functions to poll Kafka topics directly-
C. Use a custom connector to integrate Kafka with Azure Functions.
D. Use Azure Event Hubs to connect Kafka to Azure Functions-
E. Utilize Azure Data Factory to create pipelines for data movement between Kafka and Azure Functions.

正解：D

解説：
Azure Event Hubs is the recommended approach for connecting Kafka to Azure Functions for real-time streaming data ingestion. It offers high throughput and scalability, making it ideal for handling large volumes of streaming events. Service Bus (B) is suitable for message queuing but not primarily designed for streaming data. Directly polling Kafka topics (C) can be inefficient and might not scale well. Custom connectors (D) can be complex and might lack the required functionality for streaming data ingestion. Data Factory (E) is more focused on data movement and transformation, not real-time streaming

質問 # 82
You have a Kubernetes cluster running on AWS. You want to configure a persistent volume claim (PVC) that uses an AWS EBS volume for storage. Which annotation can be used to specify the EBS volume type?

A. volume.beta.kubernetes.io/aws-ebs-volume-encrypted
B. volume.beta.kubernetes.io/aws-ebs-volume-type
C. volume.beta.kubernetes.io/storage-class
D. volume.beta.kubernetes.io/aws-ebs-volume-size
E. volume.beta.kubernetes.io/storage-provisioner

正解：B

解説：
The annotation •volume.beta.kubernetes.io/aws-ebs-volume-type• is used to specify the EBS volume type (e.g., 'gp2 , 701', 'standard') when using an AWS EBS volume for persistent storage. Option 'A' is used to specify the storage class for the PVC. Option 'B' specifies the storage provisioner, which is responsible for creating the volume. Option 'D' is used to specify the size of the EBS volume. Option 'E' is for specifying whether the EBS volume should be encrypted.

質問 # 83
Consider the following Istio configuration snippet:

What does this configuration achieve?

A. It creates a virtual service that routes all requests to the 'productcatalog.example-com' service only if they match the URI prefix '/products'
B. It defines a virtual service named 'productcatalog' in the 'default' namespace and forwards all traffic to the 'productcatalog.example-com' service-
C. It configures a virtual service that only routes traffic to the 'productcatalog-v2.example.com' service for requests that match the '/products' path
D. It enables Istio's 'fault injection' feature to simulate failures and test the service's resilience.
E. It exposes the 'productcatalog' service on port 80 using the 'istio-system/gateway' gateway and routes traffic to both 'productcatalog.example-com' and 'productcatalog-v2.example.com' with a 50/50 weight distributiom

正解：E

解説：
This Istio VirtualService configuration exposes the 'productcatalog' service on port 80 using the 'istio- system/gateway' gateway It routes traffic to both 'productcatalog.example.com' and 'productcatalog-v2.example.com' with a 50/50 weight distribution for requests matching the 'Iproducts' prefix. This configuration demonstrates Istio's ability to implement dynamic traffic routing and load balancing between services.

質問 # 84
Explain the difference between a Deployment and a StatefulSet in Kubernetes.

A. Deployments are used for stateless applications, while StatefulSets are used for stateful applications that require persistent storage and unique identities.
B. Deployments handle rolling updates, while StatefulSets only support manual updates.
C. Deployments are used for deploying and managing stateless applications, while StatefulSets are used for deploying and managing stateful applications.
D. Deployments are used for managing the lifecycle of a single Pod, while StatefulSets manage multiple Pods.
E. Deployments are responsible for scheduling Pods, while StatefulSets manage the lifecycle of Services.

正解：A、C

解説：
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.

質問 # 85
Explain the difference between a Docker image and a Docker container. Provide practical scenarios where they are used.

A. Docker images and containers are the same thing, just different names.
B. A Docker image is a blueprint for creating a container, while a container is a running instance of that image.
C. Docker images are used for storing data, while containers are used for running applications.
D. Docker images are used for running applications, while containers are used for storing data.
E. A Docker container is a blueprint for creating an image, while an image is a running instance of that container.

正解：B

解説：
A Docker image is a static, immutable template that contains all the necessary components (files, libraries, dependencies, etc.) to run a specific application. Think of it as a blueprint. A Docker container is a running instance of that image, meaning it's an actual process running on your system. You can create multiple containers from the same image. Practical Scenarios: Image: You create a Docker image for a web server application, including the web server software, configuration files, and application code. This image can be shared with others or deployed to different environments. Container: You run multiple instances of this web server image as containers on your server. Each container gets its own isolated environment, allowing you to scale your web application easily.

質問 # 86
......

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