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私たちLinux FoundationのCKAD学習クイズは、仕上げの体系的な分析の分野での近年の試験状況のさまざまな専門家から作られ、学生の要求をできるだけ満たし、同時にチェックとレビューを行う専門スタッフがいますCKAD実践教材、学生の学習に高品質の情報を楽しんでもらいました。 試験の多様性により、PassTestのCKAD学習教材もさまざまな種類の学習教材にまとめられているため、学生は必要なCKADガイド急流の情報をすばやく見つけることができます。
| Certification Vendor: | Linux Foundation |
|---|---|
| Exam Name: | Certified Kubernetes Application Developer (CKAD) Exam |
| Exam Number: | CKAD |
| Exam Price: | $445 USD |
| Certificate Validity Period: | 2 years |
| Available Languages: | Japanese, Simplified Chinese, English |
| Real Exam Qty: | 15-20 |
| Exam Format: | Hands-on tasks, Command-line based, Performance-based |
| Related Certifications: | Certified Kubernetes Security Specialist (CKS) Certified Kubernetes Administrator (CKA) |
| Exam Duration: | 120 minutes |
| Passing Score: | 66% |
| Recommended Training: | Introduction to Kubernetes Kubernetes for Developers (LFD259) |
| Exam Registration: | Official Registration |
| Sample Questions: | Linux Foundation CKAD Sample Questions |
| Exam Way: | Online, remotely proctored |
| Pre Condition: | No formal prerequisites |
| Official Syllabus URL: | https://training.linuxfoundation.org/certification/certified-kubernetes-application-developer-ckad/ |
弊社のCKAD質問トレントは、手頃な価格であるだけでなく、市場で他の教育プラットフォームであるCKAD試験と比較して、ユーザーのインスタントアップグレードを容易にするための更新だけでなく、完全に練習をサポートすることもできます質問は、高品質のパフォーマンスを持っていると言うことができます。 CKAD学習教材をダウンロードして学習することを後悔することは決してありません。また、最初の試行でCKAD試験に合格します。
Linux Foundation CKAD（Linux Foundation Certified Kubernetes Application Developer）認定試験は、Kubernetesクラスター上でアプリケーションを開発および展開するスキルを検証したい開発者向けの人気のある認定プログラムです。 Kubernetesは、コンテナオーケストレーションのための強力で広く使われているオープンソースプラットフォームであり、コンテナ化されたアプリケーションを管理するために組織によってますます採用されています。 CKAD認定試験は、業界で最も求められる認定の1つであり、世界中の主要企業によって認められています。
質問 # 15
Context
Context
A user has reported an aopticauon is unteachable due to a failing livenessProbe .
Task
Perform the following tasks:
* Find the broken pod and store its name and namespace to /opt/KDOB00401/broken.txt in the format:
The output file has already been created
* Store the associated error events to a file /opt/KDOB00401/error.txt, The output file has already been created. You will need to use the -o wide output specifier with your command
* Fix the issue.
正解：
解説：
Solution:
Create the Pod:
kubectl create -f http://k8s.io/docs/tasks/configure-pod-container/exec-liveness.yaml Within 30 seconds, view the Pod events:
kubectl describe pod liveness-exec
The output indicates that no liveness probes have failed yet:
FirstSeen LastSeen Count From SubobjectPath Type Reason Message
--------- -------- ----- ---- ------------- -------- ------ -------
24s 24s 1 {default-scheduler } Normal Scheduled Successfully assigned liveness-exec to worker0
23s 23s 1 {kubelet worker0} spec.containers{liveness} Normal Pulling pulling image "gcr.io/google_containers/busybox"
23s 23s 1 {kubelet worker0} spec.containers{liveness} Normal Pulled Successfully pulled image "gcr.io/google_containers/busybox"
23s 23s 1 {kubelet worker0} spec.containers{liveness} Normal Created Created container with docker id 86849c15382e; Security:[seccomp=unconfined]
23s 23s 1 {kubelet worker0} spec.containers{liveness} Normal Started Started container with docker id 86849c15382e After 35 seconds, view the Pod events again:
kubectl describe pod liveness-exec
At the bottom of the output, there are messages indicating that the liveness probes have failed, and the containers have been killed and recreated.
FirstSeen LastSeen Count From SubobjectPath Type Reason Message
--------- -------- ----- ---- ------------- -------- ------ -------
37s 37s 1 {default-scheduler } Normal Scheduled Successfully assigned liveness-exec to worker0
36s 36s 1 {kubelet worker0} spec.containers{liveness} Normal Pulling pulling image "gcr.io/google_containers/busybox"
36s 36s 1 {kubelet worker0} spec.containers{liveness} Normal Pulled Successfully pulled image "gcr.io/google_containers/busybox"
36s 36s 1 {kubelet worker0} spec.containers{liveness} Normal Created Created container with docker id 86849c15382e; Security:[seccomp=unconfined]
36s 36s 1 {kubelet worker0} spec.containers{liveness} Normal Started Started container with docker id 86849c15382e
2s 2s 1 {kubelet worker0} spec.containers{liveness} Warning Unhealthy Liveness probe failed: cat: can't open '/tmp/healthy': No such file or directory Wait another 30 seconds, and verify that the Container has been restarted:
kubectl get pod liveness-exec
The output shows that RESTARTS has been incremented:
NAME READY STATUS RESTARTS AGE
liveness-exec 1/1 Running 1 m
質問 # 16
Context
Task
Create a new deployment for running.nginx with the following parameters;
* Run the deployment in the kdpd00201 namespace. The namespace has already been created
* Name the deployment frontend and configure with 4 replicas
* Configure the pod with a container image of lfccncf/nginx:1.13.7
* Set an environment variable of NGINX__PORT=8080 and also expose that port for the container above
正解：
解説：
Solution:
質問 # 17
You have a Deployment named 'my-app-deployment' running an application that requires a specific version of a database. This version is available in a private Docker registry with access credentials stored in a Secret. How would you configure the Deployment to pull the database image from the private registry using the Secret's credentials?
正解：
解説：
See the solution below with Step by Step Explanation.
Explanation:
Solution (Step by Step) :
1. Create a Secret:
- Create a secret containing the usemame and password required to access the private registry.
- Replace 'your-registry-username' and 'your-registry-password' with your actual credentials.
2. Update the Deployment - Modify the Deployment configuration to include the 'imagePullSecrets' field. - Add the name oftne secret you created in the previous step. - Replace 'your-private-registry-domain/your-database-image:your-version' with the actual image name and version.
3. Apply the Changes: - Apply the updated Deployment configuration using 'kubectl apply -f my-app-deployment.yamr. 4. Verify the Pull: - Check the logs of the Pods in the Deployment. You should see messages indicating that the database image is pulled from the private registry using the provided credentials.
質問 # 18
You are deploying a microservice that handles image processing tasks. The service requires a significant amount of resources, including both CPU and memory. To optimize resource utilization and ensure efficient scaling, you want to leverage Kubernetes' resource management features. Design a deployment strategy that leverages Kubernetes resources to manage and optimize the image processing service.
正解：
解説：
See the solution below with Step by Step Explanation.
Explanation:
2. Define Resource Requests and Limits: - Set resource requests and limits for your image processing containers- Requests define the minimum resources that each container needs to run smoothly, while limits define the maximum resources it can consume. This ensures that the service doesn't starve other workloads on the cluster and doesn't consume excessive resources. 3. Implement Horizontal Pod Autoscaling (HPA): - Configure HPA to automatically scale tne number of pods based on CPU or memory utilization. This enables the service to scale up during peak periods and scale down during low utilization to optimize resource usage. 4. Use Resource Quotas: - Implement Resource Quotas at the namespace level to limit the total resources that can be consumed by the image processing service and its associated workloads. This helps prevent resource starvation for other applications within the same namespace. 5. Utilize Node Affinity and Tolerations: - Apply node affinity and tolerations to schedule the image processing service on nodes that have the necessary resources (like GPLJs or high- performance CPUs) to efficiently handle image processing tasks- 6. Consider Using GPU Resources: - If your image processing tasks involve heavy computations, consider leveraging GPUs for accelerated processing. You can configure Kubernetes to schedule pods with GPU resources, ensuring that the image processing service nas access to tne necessary hardware for optimal performance.
質問 # 19 
Task:
The pod for the Deployment named nosql in the craytisn namespace fails to start because its container runs out of resources.
Update the nosol Deployment so that the Pod:
1) Request 160M of memory for its Container
2) Limits the memory to half the maximum memory constraint set for the crayfah name space.
正解：
解説：
See the solution below.
Explanation
Solution:
質問 # 20
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CKAD試験準備: https://www.passtest.jp/Linux-Foundation/CKAD-shiken.html
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