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| Section | Weight | Objectives |
|---|---|---|
| Services and Networking | 20% | - Provide and troubleshoot access to applications via services - Demonstrate basic understanding of NetworkPolicies - Use Ingress rules to expose applications |
| Application Environment, Configuration and Security | 25% | - Discover and use resources that extend Kubernetes (CRD, Operators) - ServiceAccounts - ConfigMaps and Secrets - SecurityContexts - Understanding and defining resource requirements, limits and quotas - Understand authentication, authorization and admission control |
| Application Observability and Maintenance | 15% | - Debugging in Kubernetes - Use built-in CLI tools to monitor Kubernetes applications - Utilize container logs - Implement probes and health checks - Understand API deprecation policies |
| Application Design and Build | 20% | - Choose and use the right workload resource (Deployment, DaemonSet, CronJob, etc.) - Utilize persistent and ephemeral volumes - Understand multi-container Pod design patterns (e.g., sidecar, init and others) - 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 |
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NEW QUESTION # 179
You have a Deployment named 'wordpress-deployment' that runs 3 replicas of a Wordpress container. You need to set up resource requests and limits for CPU and memory for each pod. The application requires a minimum of 250m CPU and 512Mi memory, but it should not exceed 500m CPU and IGi memory. Furthermore, implement a resource quota for the swordpress' namespace to ensure that the total CPU requests and limits do not exceed 2 CPIJ and 4Gi memory, respectively.
Answer:
Explanation:
See the solution below with Step by Step Explanation.
Explanation:
Solution (Step by Step) :
1. Update the Deployment YAML:
- Add 'resources' to the 'containers' section, setting 'requests' and 'limits' for both CPIJ and memory.
- The 'requests' should be set to the minimum required resources, and 'limits' to the maximum allowed.
2. Create the Deployment: - Apply the updated YAML file using 'kubectl apply -f wordpress-deployment-yamr 3. Create a Resource Quota: - Create a ResourceQuota YAML file named 'wordpress-quota.yaml' with the following contents:
4. Apply the Resource Quota: - Apply the ResourceQuota YAML file using 'kubectl apply -f wordpress-quota.yamr. 5. Verify the Deployment and Quota: - Check the status of the Deployment using 'kubectl get deployments wordpress-deployment' to confirm the updated resource requests and limits. - Use ' kubectl get resourcequotas -n wordpresS to verify the applied resource quota and its remaining limits- 6. Test Resource Limits: - Try to create a new Deployment or Pod in the 'wordpress' namespace that exceeds the resource quota limits. This should result in an error, ensuring the quota is enforced.
NEW QUESTION # 180
Context
An existing web application must be exposed externally.
You must connect to the correct host . Failure to do so may result in a zero score.
[candidate@base] $ ssh ckad00025
An application externally using the URL external.sterling-bengal.local . Any requests starting with / must be routed to the application web-app.
To test the web application's external reachability, run
[candidate@ckad00025]
$ curl http://external.sterling-bengal.local/
or open this URL in the remote desktop's browser.
Answer:
Explanation:
See the Explanation below for complete solution.
Explanation:
ssh ckad00025
You need to expose the existing app "web-app" externally at:
* Host: external.sterling-bengal.local
* Path: / (and anything starting with /) # route to web-app
In CKAD labs, this is almost always done with an Ingress pointing to the Service web-app.
1) Find where web-app Service lives (namespace + port)
kubectl get svc -A | grep -w web-app
You'll get something like:
<NAMESPACE> web-app ClusterIP ... <PORT>/TCP
Set the namespace:
NS=<NAMESPACE>
Check the service port(s):
kubectl -n $NS get svc web-app -o yaml
Note the service port number (commonly 80).
Also verify it has endpoints (so it actually routes to pods):
kubectl -n $NS get endpoints web-app -o wide
If endpoints are empty, the Service selector doesn't match pods - tell me and I'll give the exact fix. But usually it's fine.
2) Create the Ingress to route / to web-app
Create a manifest (use the service port you saw; I'll assume 80 below):
cat <<'EOF' > web-app-ingress.yaml
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
name: web-app-ingress
spec:
rules:
- host: external.sterling-bengal.local
http:
paths:
- path: /
pathType: Prefix
backend:
service:
name: web-app
port:
number: 80
EOF
Apply it:
kubectl -n $NS apply -f web-app-ingress.yaml
Verify:
kubectl -n $NS get ingress web-app-ingress
kubectl -n $NS describe ingress web-app-ingress
If your Service port is not 80, change number: 80 to the correct value and re-apply.
3) Test external reachability (as instructed)
Run exactly:
curl
-i http://external.sterling-bengal.local/
If curl still fails (quick checks)
A) Is there an ingress controller running?
kubectl get pods -A | egrep -i 'ingress|nginx'
kubectl get svc -A | egrep -i 'ingress|nginx'
B) Does Ingress show an address?
kubectl -n $NS get ingress web-app-ingress -o wide
C) Do we have endpoints?
kubectl -n $NS get endpoints web-app -o wide
NEW QUESTION # 181
You have a Deployment named 'my-app-deployment running a Flask application. You want to add a liveness probe that checks if the Flask application is responding on port '5000' and a readiness probe that checks if the application is ready to receive requests. Implement these probes using Kustomize.
Answer:
Explanation:
See the solution below with Step by Step Explanation.
Explanation:
Solution (Step by Step) :
1. Create a base Deployment configuration:
2. Create a 'kustomization.yamr file:
3. Create 'patcnes/liveness-probe.yaml':
4. Create 'patches/readiness-probe-yaml':
5. Apply the Kustomize configuration: bash kustomize . I kubectl apply -t- - Liveness probe: This probe checks if the application is still alive and running. It uses a TCP socket to connect to port ' 5000' and waits for 15 seconds before making the first cneck. It checks every 20 seconds, and if it fails 3 times in a row, the pod is restarted. - Readiness probe: This probe checks if the application is ready to receive requests. It also uses a TCP socket to connect to port '5000'. It checks every 10 seconds and waits for 5 seconds before the first check. If it fails 2 times in a row, the pod is marked as unhealthy and excluded trom receiving traffic. Note: Make sure your Flask application is actually listening on port '5000' and responding to requests. ,
NEW QUESTION # 182
You are building a container image for a Python application that requires several external libraries. You want to ensure that the image is as small as possible while still containing all necessary dependencies. What strategy should you use to optimize the image size? Explain your approach and provide a code example.
Answer:
Explanation:
See the solution below with Step by Step Explanation.
Explanation:
Solution (Step by Step) :
I). Use a multi-stage Dockerfile: This allows you to have separate build and runtime stages. The build stage can include all necessary tools and dependencies for building the application, while the runtime stage only includes the essential components needed to run the application.
2. Minimize the base image: Choose a base image With only the necessary operating system components, tools, and libraries. I-Ising a slim image variant like 'python:3.9-slim' reduces the image Size significantly. 3. Use a lightweight package manager: Employ a lightweight package manager like 'pip' for installing Python dependencies. This helps keep the image lean 4. Optimize dependencies: Analyze your 'requirements.txt' file and remove any unnecessary dependencies or packages. This is crucial for reducing the overall size of the image. 5. Use caching wisely: In the 'Dockerfile', leverage caching by placing 'COPY commands for your application code before 'RUN' commands. This prevents unnecessary rebuilds of the image when only the application code changes. 6. Consider dependency bundling: If your application relies on specific library versions, consider using a tool like 'pip-tools' to lock down dependencies. This avoids issues where updates to external libraries introduce compatibility problems. 7. Remove unnecessary files: After building your image, inspect the image layers and identify any unneeded files. Remove these files using 'docker image prune' to further reduce image size.
NEW QUESTION # 183
You are working on a Kubernetes application that requires a scheduled job to run a data processing script every day at midnight. The script takes approximately 30 minutes to complete and requires access to a persistent volume to store its output dat a. How would you create a Job resource that meets these requirements?
Answer:
Explanation:
See the solution below with Step by Step Explanation.
Explanation:
Solution (Step by Step) :
I). Create a Persistent Volume Claim:
- Define a Persistent Volume Claim (PVC) to request the necessary storage space.
- Specify the access mode and storage class according to your cluster configuration.
2. Define the Job Resource: - Create a Job resource With a 'cronJob' schedule to trigger the execution at midnight every day. - Specify the 'backoffLimit' to control the number of retries in case of failures. - Define the 'spec-template.spec.containers' section for the container running the data processing script. - Mount the PVC to the container using 'volumeMounts'.
3. Create the Job: - Apply the Job YAML file using 'kubectl apply -f data-processing-job.yamr 4. Verify Job Execution: - Use 'kubectl get jobS to monitor the status of the Job. - Check the 'status. completionTime' to verify that the Job completed successfully. - Verity that the output data is stored in the mounted persistent volume. 5. Update the Script - Update the 'your-data-processing-script.sh" with the necessary commands to process the data and store the output in the "ldata' directory. 6. Monitor the Job: - Continuously monitor the Job's status and logs using 'kubectl logs' to ensure it runs correctly. Note: Replace 'your-image-namelatest and 'your-data-processing-scriptsh' with the actual image name and script file respectively,
NEW QUESTION # 184
......
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