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The CKAD certification exam is a hands-on test that requires candidates to perform a variety of tasks related to Kubernetes application development. CKAD exam is conducted online, and candidates are required to complete a set of performance-based tasks within two hours. CKAD Exam Tests the candidate's ability to create and configure Kubernetes objects, deploy applications, troubleshoot issues, and manage Kubernetes resources.

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CKAD Test Sample Questions & Test CKAD Lab Questions

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The CKAD Exam is an industry-recognized certification that demonstrates a candidate's proficiency in Kubernetes application development. It is an ideal certification for IT professionals who want to enhance their skills in cloud-native application development and deployment. Linux Foundation Certified Kubernetes Application Developer Exam certification validates a candidate's knowledge of Kubernetes resources, application design and development, debugging, troubleshooting, and security.

The CKAD certification is a valuable credential for developers who are looking to advance their career in the field of Kubernetes and containerization. Linux Foundation Certified Kubernetes Application Developer Exam certification demonstrates that the candidate has the skills and knowledge required to design, build, and deploy Kubernetes-based applications, and can effectively use Kubernetes to manage containerized applications. The CKAD Certification is recognized by industry leaders and provides a competitive edge to the certified professional in the job market.

Linux Foundation Certified Kubernetes Application Developer Exam Sample Questions (Q127-Q132):

NEW QUESTION # 127
You are creating a Deployment for a web application that uses a database for its data persistence. You want to scale the deployment horizontally, but you also want to ensure that each pod has access to the same database instance. Explain how you can use a ConfigMap to provide database connection details to each pod.

Answer:

Explanation:
See the solution below with Step by Step Explanation.
Explanation:
Solution (Step by Step) :
1. Create a ConfigMap:
- Create a ConfigMap that contains the necessary database connection details.
- This includes parameters like host, port, username, password, and database name.
- Example:

2. Mount the ConfigMap: - In your Deployment's 'spec-template-spec-containers' , mount the ConfigMap as a volume. - Example:

3. Access ConfigMap Values: - Within your application's code, use environment variables to access the values from the mounted ConfigMap. - For example, the environment variable 'DB HOST' would be set to the value "database-service" from the ConfigMap. 4. Deploy and Test: - Apply the ConfigMap and Deployment YAML files. - Test the application to confirm that each pod can connect to the same database using the connection details provided by the ConfigMap. 5. Scale the Deployment: - Scale the Deployment horizontally to increase the number of replicas. - Each new pod will automatically inherit the mounted ConfigMap and have access to the same database connection details.

NEW QUESTION # 128
You have a Deployment named 'wordpress-deployment' that runs 3 replicas of a WordPress container. The container image is pulled from a private registry with authentication credentials stored in a secret called 'registry-credentials' _ Implement a strategy to ensure that only one replica of the Deployment is updated at a time, and the update process is triggered automatically whenever a new image is pushed to the private registry with the tag 'wordpress:latest'

Answer:

Explanation:
See the solution below with Step by Step Explanation.
Explanation:
Solution (Step by Step) :
1. Create a Secret for Registry Credentials:

- Replace with the base64 encoded content of your dockerconfigjson file containing the registry credentials. 2 Update the Deployment YAML:

- Replace 'your-registry-com' with your private registry address. - Ensure the 'imagePullSecrets' section references tne 'registry-credentials' secret. - Configure the 'strategy-type' to 'Rollingupdate', and 'strategy-rollingl-lpdate.maxunavailable' to ' 1 ' to allow only one replica to be unavailable during the update. - Set 'imagePullPolicy' to 'Always' to ensure the new image is pulled every time. 3. Apply the Updated YAML: bash kubectl apply -f wordpress-deploymentyaml 4. Verify the Deployment bash kubectl get deployments wordpress-deployment 5. Trigger the Automatic Update: - Push a new image with the tag 'wordpress:latest' to your private registry. 6. Monitor the Deployment - Use 'kubectl get pods -l app=wordpress' to monitor the pod updates during the rolling update process. 7. Check for Successful Update: - After the update is complete, run 'kubectl describe deployment wordpress-deployment' to verify that the 'updatedReplicaS matcnes the 'replicas' field. Now, whenever a new image tagged 'wordpress:latest' is pushed to your private registry, Kubernetes will automatically trigger a rolling update for the 'wordpress-deployment , updating one replica at a time while ensuring availability.,

NEW QUESTION # 129
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 # 130
Exhibit:

Context
A pod is running on the cluster but it is not responding.
Task
The desired behavior is to have Kubemetes restart the pod when an endpoint returns an HTTP 500 on the /healthz endpoint. The service, probe-pod, should never send traffic to the pod while it is failing. Please complete the following:
* The application has an endpoint, /started, that will indicate if it can accept traffic by returning an HTTP 200. If the endpoint returns an HTTP 500, the application has not yet finished initialization.
* The application has another endpoint /healthz that will indicate if the application is still working as expected by returning an HTTP 200. If the endpoint returns an HTTP 500 the application is no longer responsive.
* Configure the probe-pod pod provided to use these endpoints
* The probes should use port 8080

A. Solution:

In the configuration file, you can see that the Pod has a single Container. The periodSeconds field specifies that the kubelet should perform a liveness probe every 5 seconds. The initialDelaySeconds field tells the kubelet that it should wait 5 seconds before performing the first probe. To perform a probe, the kubelet executes the command cat /tmp/healthy in the target container. If the command succeeds, it returns 0, and the kubelet considers the container to be alive and healthy. If the command returns a non-zero value, the kubelet kills the container and restarts it.
When the container starts, it executes this command:
/bin/sh -c "touch /tmp/healthy; sleep 30; rm -rf /tmp/healthy; sleep 600"
For the first 30 seconds of the container's life, there is a /tmp/healthy file. So during the first 30 seconds, the command cat /tmp/healthy returns a success code. After 30 seconds, cat /tmp/healthy returns a failure code.
Create the Pod:
kubectl apply -f https://k8s.io/examples/pods/probe/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 "k8s.gcr.io/busybox"
23s 23s 1 {kubelet worker0} spec.containers{liveness} Normal Pulled Successfully pulled image "k8s.gcr.io/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 "k8s.gcr.io/busybox"
36s 36s 1 {kubelet worker0} spec.containers{liveness} Normal Pulled Successfully pulled image "k8s.gcr.io/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 1m
B. Solution:

In the configuration file, you can see that the Pod has a single Container. The periodSeconds field specifies that the kubelet should perform a liveness probe every 5 seconds. The initialDelaySeconds field tells the kubelet that it should wait 5 seconds before performing the first probe. To perform a probe, the kubelet executes the command cat /tmp/healthy in the target container. If the command succeeds, it returns 0, and the kubelet considers the container to be alive and healthy. If the command returns a non-zero value, the kubelet kills the container and restarts it.
When the container starts, it executes this command:
/bin/sh -c "touch /tmp/healthy; sleep 30; rm -rf /tmp/healthy; sleep 600"
For the first 30 seconds of the container's life, there is a /tmp/healthy file. So during the first 30 seconds, the command cat /tmp/healthy returns a success code. After 30 seconds, cat /tmp/healthy returns a failure code.
Create the Pod:
kubectl apply -f https://k8s.io/examples/pods/probe/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 "k8s.gcr.io/busybox"
23s 23s 1 {kubelet worker0} spec.containers{liveness} Normal Pulled Successfully pulled image "k8s.gcr.io/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 "k8s.gcr.io/busybox"
36s 36s 1 {kubelet worker0} spec.containers{liveness} Normal Pulled Successfully
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 1m

Answer: A

NEW QUESTION # 131
Exhibit:

Given a container that writes a log file in format A and a container that converts log files from format A to format B, create a deployment that runs both containers such that the log files from the first container are converted by the second container, emitting logs in format B.
Task:
* Create a deployment named deployment-xyz in the default namespace, that:
* Includes a primary
lfccncf/busybox:1 container, named logger-dev
* includes a sidecar Ifccncf/fluentd:v0.12 container, named adapter-zen
* Mounts a shared volume /tmp/log on both containers, which does not persist when the pod is deleted
* Instructs the logger-dev
container to run the command

which should output logs to /tmp/log/input.log in plain text format, with example values:

* The adapter-zen sidecar container should read /tmp/log/input.log and output the data to /tmp/log/output.* in Fluentd JSON format. Note that no knowledge of Fluentd is required to complete this task: all you will need to achieve this is to create the ConfigMap from the spec file provided at /opt/KDMC00102/fluentd-configma p.yaml , and mount that ConfigMap to /fluentd/etc in the adapter-zen sidecar container

A. Solution:
B. Solution:

Answer: A

NEW QUESTION # 132
......

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