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| Section | Weight | Objectives |
|---|---|---|
| Application Design and Build | 20% | - Utilize persistent and ephemeral volumes - Understand multi-container Pod design patterns (e.g., sidecar, init and others) - Choose and use the right workload resource (Deployment, DaemonSet, CronJob, etc.) - Define, build and modify container images |
| Services and Networking | 20% | - Use Ingress rules to expose applications - Provide and troubleshoot access to applications via services - Demonstrate basic understanding of NetworkPolicies |
| Application Environment, Configuration and Security | 25% | - Discover and use resources that extend Kubernetes (CRD, Operators) - ServiceAccounts - ConfigMaps and Secrets - Understanding and defining resource requirements, limits and quotas - SecurityContexts - Understand authentication, authorization and admission control |
| Application Deployment | 20% | - Understand Deployments and how to perform rolling updates - Use the Helm package manager to deploy existing packages - Kustomize - Use Kubernetes primitives to implement common deployment strategies (e.g., blue/green or canary) |
| Application Observability and Maintenance | 15% | - Debugging in Kubernetes - Utilize container logs - Understand API deprecation policies - Use built-in CLI tools to monitor Kubernetes applications - Implement probes and health checks |
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NEW QUESTION # 144
Context
You are asked to scale an existing application and expose it within your infrastructure.
First, update the Deployment nginx-deployment in the prod
namespace :
. to run 2 replicas of the Pod
. add the following label to the Pod :
role: webFrontEnd
Next, create a NodePort Service named rover in the prod namespace exposing the nginx-deployment Deployment 's Pods See the Explanation below for complete solution.
Answer:
Explanation:
Below is an exam-style, step-by-step solution (commands + verification). Follow exactly on host ckad000.
0) Connect to the right host
ssh ckad000
(Optional but good sanity check)
kubectl config current-context
kubectl get ns
1) Inspect the existing Deployment (to know its labels/ports)
kubectl -n prod get deploy nginx-deployment
kubectl -n prod get deploy nginx-deployment -o wide
Check what labels the Pod template already has (important for the Service selector):
kubectl -n prod get deploy nginx-deployment -o jsonpath='{.spec.template.metadata.labels}{"\n"}' Check container ports (so we expose the correct targetPort):
kubectl -n prod get deploy nginx-deployment -o jsonpath='{.spec.template.spec.containers[0].ports}{"\n"}' If ports output is empty, it's still often nginx on 80, but the safest is to confirm by describing a pod later.
2) Update Deployment to 2 replicas
Fastest:
kubectl -n prod scale deploy nginx-deployment --replicas=2
Verify:
kubectl -n prod get deploy nginx-deployment
3) Add label role=webFrontEnd to the Pod (Pod template label)
You must add it under:
spec.template.metadata.labels
Use a patch (quick + safe):
kubectl -n prod patch deploy nginx-deployment \
-p '{"spec":{"template":{"metadata":{"labels":{"role":"webFrontEnd"}}}}}' Verify the Deployment template now includes it:
kubectl -n prod get deploy nginx-deployment -o jsonpath='{.spec.template.metadata.labels}{"\n"}' Now verify the running Pods have the label (important!):
kubectl -n prod get pods --show-labels
If the label doesn't show on pods immediately, wait for rollout:
kubectl -n prod rollout status deploy nginx-deployment
kubectl -n prod get pods --show-labels
4) Create a NodePort Service rover exposing the Deployment's Pods
4.1 Get a reliable target port
Try to read containerPort:
kubectl -n prod get deploy nginx-deployment -o jsonpath='{.spec.template.spec.containers[0].ports[0].
containerPort}{"\n"}'
* If this prints a number (commonly 80), use it as --target-port.
* If it prints nothing/empty, check a pod:
POD=$(kubectl -n prod get pod -l role=webFrontEnd -o jsonpath='{.items[0].metadata.name}') kubectl -n prod describe pod "$POD" | sed -n '/Containers:/,/Conditions:/p' | sed -n '/Ports:/,/Environment:/p' Assuming nginx is on 80 (most common), create the service:
kubectl -n prod expose deploy nginx-deployment \
--name=rover \
--type=NodePort \
--port=80 \
--target-port=80
If your nginx container port is different (e.g., 8080), change --target-port=8080 accordingly.
5) Verify Service + endpoints (critical)
kubectl -n prod get svc rover -o wide
kubectl -n prod describe svc rover
kubectl -n prod get endpoints rover -o wide
You should see 2 endpoints (matching 2 pods).
Also confirm the pods are Ready:
kubectl -n prod get pods -l role=webFrontEnd -o wide
Quick "CKAD checkpoints"
* Deployment in prod has replicas=2
* Pod template has label role=webFrontEnd
* Service rover in prod is NodePort
* Service endpoints point to the nginx pods
NEW QUESTION # 145
You are building a data processing pipeline that involves multiple steps. Each step is implemented as a separate container image. The pipeline snould run only once, and it should nandle errors gracefully by retrying failed steps. How can you design this pipeline using Kubernetes Jobs, and how would you handle error handling and retries?
Answer:
Explanation:
See the solution below with Step by Step Explanation.
Explanation:
Solution (Step by Step) :
1. Define a Pipeline with Multiple Jobs:
- Create a Job for each stage in your data processing pipeline.
- Each Job should have a dedicated container image specific to its processing step.
2. Implement Error Handling:
- Retry Mechanism Use the 'backoffLimit' and 'retries' settings within each Job's 'spec-template-spec-containers' to specify the number of retries and the delay between retries for each step.
- Error Logging: Ensure each Job logs errors to a centralized location (e.g., a persistent volume) for debugging and analysis. You can use a sidecar container to collect and process logs.
3. Chain Jobs:
- Use a Kubernetes 'Job' to chain the individual steps, ensuring that each step runs successfully before moving to the next.
- For example, use a script within the first Job's container to trigger the next Job once it completes.
4. Example Code (Simplified):
5. Execute the Pipeline: - Run the first Job ('data-extraction'). - If it fails, it will retry up to 'backoffLimit' times. - Once successful, it can trigger the second Job ('data-transformation') using a script in its container or by creating a dependent Job. 6. Monitoring and Logging: - Use Kubernetes dashboards to monitor the progress of each Job. - Check logs for error messages and debug failures. - Implement a centralized logging solution to collect logs from all Jobs. Note: For more complex pipelines, you can consider using tools like Argo Workflows or Tekton Pipelines for more advanced orchestration and error handling capabilities.,
NEW QUESTION # 146
Context
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
Answer:
Explanation:
Solution:
NEW QUESTION # 147 
Task
You are required to create a pod that requests a certain amount of CPU and memory, so it gets scheduled to-a node that has those resources available.
* Create a pod named nginx-resources in the pod-resources namespace that requests a minimum of 200m CPU and 1Gi memory for its container
* The pod should use the nginx image
* The pod-resources namespace has already been created
Answer:
Explanation:
See the solution below.
Explanation
Solution:
NEW QUESTION # 148
Refer to Exhibit.
Set Configuration Context:
[student@node-1] $ | kubectl
Config use-context k8s
Context
You sometimes need to observe a pod's logs, and write those logs to a file for further analysis.
Task
Please complete the following;
* Deploy the counter pod to the cluster using the provided YAMLspec file at /opt/KDOB00201/counter.yaml
* Retrieve all currently available application logs from the running pod and store them in the file /opt/KDOB0020l/log_Output.txt, which has already been created
Answer:
Explanation:
Solution:
To deploy the counter pod to the cluster using the provided YAML spec file, you can use the kubectl apply command. The apply command creates and updates resources in a cluster.
kubectl apply -f /opt/KDOB00201/counter.yaml
This command will create the pod in the cluster. You can use the kubectl get pods command to check the status of the pod and ensure that it is running.
kubectl get pods
To retrieve all currently available application logs from the running pod and store them in the file /opt/KDOB0020l/log_Output.txt, you can use the kubectl logs command. The logs command retrieves logs from a container in a pod.
kubectl logs -f <pod-name> > /opt/KDOB0020l/log_Output.txt
Replace <pod-name> with the name of the pod.
You can also use -f option to stream the logs.
kubectl logs -f <pod-name> > /opt/KDOB0020l/log_Output.txt &
This command will retrieve the logs from the pod and write them to the /opt/KDOB0020l/log_Output.txt file.
Please note that the above command will retrieve all logs from the pod, including previous logs. If you want to retrieve only the new logs that are generated after running the command, you can add the --since flag to the kubectl logs command and specify a duration, for example --since=24h for logs generated in the last 24 hours.
Also, please note that, if the pod has multiple containers, you need to specify the container name using -c option.
kubectl logs -f <pod-name> -c <container-name> > /opt/KDOB0020l/log_Output.txt The above command will redirect the logs of the specified container to the file.
NEW QUESTION # 149
......
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