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Linux Foundation CKAD Pdf Format Practice Program

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Linux Foundation Certified Kubernetes Application Developer Exam Sample Questions (Q159-Q164):

NEW QUESTION # 159

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

Answer:

Explanation:
See the solution below.
Explanation
Solution:
apiVersion: v1
kind: Pod
metadata:
labels:
test: liveness
name: liveness-exec
spec:
containers:
- name: liveness
image: k8s.gcr.io/busybox
args:
- /bin/sh
- -c
- touch /tmp/healthy; sleep 30; rm -rf /tmp/healthy; sleep 600
livenessProbe:
exec:
command:
- cat
- /tmp/healthy
initialDelaySeconds: 5
periodSeconds: 5
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

NEW QUESTION # 160
You have a Deployment named 'my-app-deployment' running three replicas of an application container. You need to implement a rolling update strategy were only one pod is updated at a time. Additionally, you need to ensure tnat tne update process is triggered automatically whenever a new image is pushed to your private Docker registry.

Answer:

Explanation:
See the solution below with Step by Step Explanation.
Explanation:
Solution (Step by Step) :
1. Update the Deployment YAML:
- Update the 'replicas' to 2.
- Define 'maxunavailable: 1' and 'maxSurge: O' in the 'strategy-rollinglJpdate' section to control the rolling update process.
- Configure a 'strategy.types to 'Rollingupdates to trigger a rolling update wnen the deployment is updated.
- Add a 'spec-template.spec.imagePullP01icy: Always' to ensure tnat tne new image is pulled even if it exists in the pod's local cache.
- Add a 'spec-template-spec-imagePullSecrets' section to provide access to your private Docker registry. Replace 'registry-secret with the actual name of your secret.

2. Create the Deployment - Apply the updated YAML file using 'kubectl apply -f my-app-deployment.yamr 3. Verify the Deployment: - Check the status of the deployment using 'kubectl get deployments my-app-deployment' to confirm the rollout and updated replica count. 4. Trigger the Automatic Update: - Push a new image to your private Docker registry with a tag like 'your-private-registry.com/your-namespacemy-app:latest. 5. Monitor the Deployment: - Use 'kubectl get pods -l app=my-apps to monitor the pod updates during the rolling update process. You will observe that one pod is terminated at a time, while one new pod with the updated image is created. 6. Check for Successful Update: - Once the deployment is complete, use 'kubectl describe deployment my-app-deployment' to see that the updatedReplicas' field matches the 'replicas' field, indicating a successful update. ]

NEW QUESTION # 161
You are running a web application with multiple services exposed via Kubernetes Ingress. The application has two distinct environments: 'staging' and 'production' , each with its own set of services and domain names. You need to configure Ingress rules to route traffic to the appropriate services based on the requested hostname and environment. For example, requests to 'staging.example.com' should be directed to the staging environment, while requests to 'example.com' should go to the production environment. Implement this configuration using Ingress rules.

Answer:

Explanation:
See the solution below with Step by Step Explanation.
Explanation:
Solution (Step by Step) :
1. Create a Service for Each Environment:
- Define services for both 'staging' and 'production' environments, ensuring that the services for each environment are named appropriately. For example, 'staging-service' and .

2. Create an Ingress Resource: - Define an Ingress resource that maps the hostnames to the corresponding services.

3. Apply the Configuration: - Apply the service and ingress definitions using 'kubectl apply -f services.yaml' and 'kubectl apply -f ingress.yaml' respectively. 4. Test the Configuration: - Access 'staging.example.com' and 'example.com' in your browser to ensure that the traffic is directed to the correct services and environments. ,

NEW QUESTION # 162
You have a container image for your application that includes both the application code and its dependencies. However, you've noticed that the image size is becoming increasingly large. How would you optimize tne container image to reduce its size and improve deployment efficiency?

Answer:

Explanation:
See the solution below with Step by Step Explanation.
Explanation:
Solution (Step by Step) :
1. Identify and remove unnecessary files: Review the contents ot the image to identify any files that are not required at runtime. This may include development tools, build scripts, documentation, or temporary files. I-Jse a tool like 'docker history' to see the layers of the image and identify unnecessary additions.
2. Optimize build steps: Analyze your Dockerfile and identify any unnecessary commands or layers that contribute to image size. For instance, using multi-stage builds to separate build dependencies from runtime dependencies can significantly reduce image size.
3. Use smaller base images: Choose a leaner base image like 'alpine' or 'scratch' (for minimal environments) instead of a large, bloated base image like 'ubuntu' or 'centos'. Smaller base images offer a significant advantage in terms ot image size-
4. Compress files: Compress static assets, such as configuration files or log files, using tools like 'gzip' or 'bzip2 to reduce their size.
5. Employ a package manager for dependencies: Utilize a package manager like 'apt-gets or 'yum' to install necessary libraries and dependencies. This helps streamline the installation process and optimize package selection.
Example:
Original Dockefflle:
FROM ubuntu:latest
# Install dependencies
RUN apt-get update &&
apt-get install -y python3 python3-pip
# Copy application code and dependencies
COPY - /app
# Run application
CMD ["pytnon3", "/app/app.py"]
Optimized Dockerfile with multi-stage build:
FROM python:3.9-alpine AS builder
# Install dependencies
COPY requirements.txt lapp,/
RUN pip install -r /app/requirements.txt
# Build the application
COPY . /app
RUN python setup.py build
FROM scratch AS runtime
# Copy the compiled application
COPY --from-builder /app/build /app
# Run the application
CMD ["/app/app"]
This optimized Dockerfile uses a smaller base image ('pytnon.3.9-alpineS), leverages multi-stage builds to separate build dependencies from runtime dependencies, and copies only the necessary compiled application to the final image. This results in a significantly smaller container image., You nave a critical batch job tnat processes large amounts of data daily. The job needs to run at a specific time every day, even if the Kubernetes cluster is restarted. Explain how you would design and implement this job using Kubernetes Jobs and CronJobs to ensure reliable execution.

NEW QUESTION # 163
You are developing a multi-container application that includes a web server, a database, and a message broker. You want to ensure that the database and message broker start before the web server to avoid dependency issues. How can you design your deployment to achieve this?

Answer:

Explanation:
See the solution below with Step by Step Explanation.
Explanation:
Solution (Step by Step) :
1. Define Pod with Containers:
- Create a 'Pod' definition with three containers: 'web-server', 'database' , and 'message-broker
- Include the appropriate image names for each container.

2. Implement Init Containers: - Define ' initcontainers' within the 'Pod' spec to run containers before the main application containers. - Use 'initContainers' to set up the database and message broker:

3. Apply the Pod Definition: - Apply the 'Pod' definition using 'kubectl apply -f multi-container-app.yamr 4. Verify Container Startup Order: - Check the pod logs using 'kubectl logs -f multi-container-app'. You will observe the init containers ('database-init and 'message-broker-init') starting first, followed by the main containers ('web-server', 'database' , and 'message-broker'). Note: In this example, the 'database-init and 'message-broker-init containers simply print a message. You can replace these with actual initialization scripts or commands relevant to your specific database and message broker services.

NEW QUESTION # 164
......

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