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| Section | Weight | Objectives |
|---|---|---|
| Application Observability and Maintenance | 15% | - Understand API deprecation policies - Implement probes and health checks - Use built-in CLI tools to monitor Kubernetes applications - Utilize container logs - Debugging in Kubernetes |
| Application Design and Build | 20% | - Choose and use the right workload resource (Deployment, DaemonSet, CronJob, etc.) - Understand multi-container Pod design patterns (e.g., sidecar, init and others) - Utilize persistent and ephemeral volumes - Define, build and modify container images |
| Application Deployment | 20% | - Use Kubernetes primitives to implement common deployment strategies (e.g., blue/green or canary) - Use the Helm package manager to deploy existing packages - Understand Deployments and how to perform rolling updates - Kustomize |
| Application Environment, Configuration and Security | 25% | - Understand authentication, authorization and admission control - SecurityContexts - Discover and use resources that extend Kubernetes (CRD, Operators) - Understanding and defining resource requirements, limits and quotas - ServiceAccounts - ConfigMaps and Secrets |
| Services and Networking | 20% | - Provide and troubleshoot access to applications via services - Demonstrate basic understanding of NetworkPolicies - Use Ingress rules to expose applications |
あなたが失敗した場合、あなたのレッスンを学ぶことを忘れないでください。 それでも自分でテストの準備をしていて、何度も失敗する場合は、有効なCKADスタディガイドを選択してください。 これは、試験をクリアして認定を取得するための最良の方法です。 優れたCKAD学習ガイドは、効率的な準備と効率的な練習への近道となります。無駄な努力を避け、興味深いことをします。 JPTestKingは、受験者が最初の試行で100％合格することを保証する100％合格率CKADスタディガイドファイルをリリースします。
質問 # 168
You have a Deployment named 'web-app' running a containerized application with a complex startup sequence. The application relies on a database service that might be Slow to respond on startup. How would you implement Liveness and Readiness probes to ensure the application iS healthy and available to users, even during startup?
正解：
解説：
See the solution below with Step by Step Explanation.
Explanation:
Solution (Step by Step) :
1. Define Liveness Probe:
- Create a 'livenessProbe' within the 'containers' section of your 'web-app' Deployment YAML-
- Choose a probe type appropriate tor your application. In this case, since the startup is complex, use an 'exec' probe.
- Specify the command to execute. This should be a simple command that checks if the application is up and ready to handle requests.
- Set 'initialDelaySecondS and 'periodSeconds' to provide sufficient time for the application to start.
- Configure 'failureThreshold' and 'successThreshold' to define how many tailed or successful probes trigger a pod restart.
2. Define Readiness Probe: - Create a 'readinessProbe' Within the 'containers' section of your 'web-apps Deployment YAML. - Use the same 'exec' probe type as for the liveness probe. - Specify a command that checks it the application is ready to serve traffic. - Set 'initialDelaySeconds' and 'periodSeconds' to control the frequency and delay of the probe. - Configure 'failureThreshold' and 'successThreshold' to handle failed or successful probe results.
3. Deploy the Deployment: - Apply the updated YAML file using 'kubectl apply -f web-app.yamr 4. Verify the Probes: - Observe the pod logs using 'kubectl logs to see when liveness and readiness probes are executed. - Use 'kubectl get pods -I app=web-app' to check the status of pods and see how liveness and readiness probes affect the pod's health and availability. 5. Test the Application: - Send requests to the application to verify that it is healthy and responsive, even during startup. - Liveness Probe: The ' livenessProbe' checks if the application is still healthy and running. If the probe fails repeatedly, the Kubernetes will restart the pod to fix the issue. This ensures that unhealthy pods are removed and replaced with healthy ones. - Readiness Probe: The 'readinessproa' cnecks it the application iS ready to receive traffic. This allows Kubernetes to delay sending traffic to a pod until it is fully initialized and prepared to serve requests. It helps prevent users from encountering errors during startup. By using both liveness and readiness probes, you can ensure your application is healthy and available to users, even during complex startup sequences.,
質問 # 169
You have a microservice application that relies on a Redis cacne for data retrieval. Design a multi-container Pod that incorporates a Redis sidecar container to provide local caching within the Pod. Ensure that the main application container can access the Redis sidecar container within the same Pod Namespace Without needing to communicate with an external Redis cluster.
正解：
解説：
See the solution below with Step by Step Explanation.
Explanation:
Solution (Step by Step) :
1. Define the Pod YAML: Create a Pod YAML file that includes both the main application container and the Redis sidecar container.
2. Configure Environment Variables: Set an environment variable 'REDIS HOST within the main application container to point to the Redis sidecar containers hostname- In Kubernetes, containers within the same Pod can communicate with each other using their container names. 3. Connect Application to Redis: Modifry' the application code to connect to the Redis instance using the 'REDIS HOST environment variable. For example, using a Python application with the 'redis-py' library: python import redis r = redis-Redis(host=os.environ.get('REDlS_HOST'), port=6379) # Perform Redis operations (e.g., r.set('key', 'value')) 4. Deploy the Pod: Apply the Pod YAML using 'kubectl apply -f my-app-pod.yamr 5. Verify Connectivity: Check the logs of the main application container to ensure it's successfully connecting to the Redis sidecar container Note: This approach provides local caching within the Pod, reducing external network calls and improving performance. It's important to consider potential data consistency issues if multiple Pods share the same Redis instance.
質問 # 170
Context
Context
You are asked to prepare a Canary deployment for testing a new application release.
Task:
A Service named krill-Service in the goshark namespace points to 5 pod created by the Deployment named current-krill-deployment
1) Create an identical Deployment named canary-kill-deployment, in the same namespace.
2) Modify the Deployment so that:
-A maximum number of 10 pods run in the goshawk namespace.
-40% of the krill-service 's traffic goes to the canary-krill-deployment pod(s)
正解：
解説：
Solution:
質問 # 171
You have a Kubernetes deployment named 'my-app' that runs an application with a specific configuration defined in a ConfigMap named 'my-config'. You need to implement a strategy to automatically update tne deployment wnen tne ConfigMap is changed.
正解：
解説：
See the solution below with Step by Step Explanation.
Explanation:
Solution (Step by Step) :
1. Create a 'ConfigMap' named 'my-configs with the following contents:
2. Create a 'Deployment' named 'my-app' that mounts the 'my-config' ConfigMap as a volume:
3. Apply the ConfigMap and Deployment bash kubectl apply -f configmap.yaml kubectl apply -f deployment.yaml 4. Update the ConfigMap with new values:
5. Apply the updated ConfigMap: bash kubectl apply -f configmap.yaml - The 'kustomization.yamr file defines the resources (the 'deployment_yamr file) and the patches to apply. - The 'deployment_yamr file contains the base configuration for the deployment. - The patch.yamr tile applies a strategic merge patch to the deployment, configuring rolling updates and automatic updates triggered by new images. - The 'maxSurge' and 'maxunavailable' settings in the 'patch_yaml' define the maximum number of pods that can be added or removed during the update process. - The 'imagePullPolicy: Always' ensures that the new image is pulled from Docker Hub even if it exists in the pod's local cache, triggering the update.
質問 # 172
You're managing a Kubernetes cluster with various applications. You want to implement a mechanism that automatically scales deployments based on CPU utilization. The scaling should be triggered when CPU utilization exceeds 70% and should scale down to 50% utilization.
正解：
解説：
See the solution below with Step by Step Explanation.
Explanation:
Solution (Step by Step) :
1. Define the Horizontal Pod Autoscaler (HPA) YAMLI
- Create an HPA YAML file named 'auto-scaler.yaml' with the following contents:
2. Apply the HPA: - Apply the HPA YAML file using 'kubectl apply -f auto-scaler.yamr. 3. Test the Auto-scaler - Monitor the CPU utilization of your deployment. When it exceeds 70%, the HPA will automatically scale up the deployment. - Observe the deployment scaling down when CPIJ utilization drops below 50%.
質問 # 173
......
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