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| Section | Objectives |
|---|---|
| Topic 1: Services and Networking | - Ingress basics - Pod networking concepts - Services (ClusterIP, NodePort) |
| Topic 2: Application Observability and Maintenance | - Understand probes and health checks - Monitor and troubleshoot applications |
| Topic 3: Application Design and Build | - Define, build and modify container images - Understand Jobs and CronJobs - Understand multi-container Pod design patterns |
| Topic 4: Application Deployment | - Understand rolling updates and rollbacks - Use Kubernetes primitives to implement deployments |
| Topic 5: Application Environment, Configuration and Security | - Security contexts and service accounts - ConfigMaps and Secrets usage |
| Topic 6: State Persistence | - Storage classes and volume mounting - PersistentVolumes and PersistentVolumeClaims |
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NEW QUESTION # 234
You have a Deployment named 'redis-deployment that runs 3 replicas of a Redis container. You need to implement a rolling update strategy that allows tor a maximum ot one pod to be unavailable at any given time during tne update process, With the new pod becoming available before the old pod is terminated. Additionally, you want to ensure that the update process is triggered automatically whenever a new image is pushed to the Docker Hub repository 'redislabs/redis:latest.
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: 1 ' in the 'strategy.rollingupdate' section to control the rolling update process.
- Configure a 'strategy-type' to 'Rollingupdate' to trigger a rolling update when the deployment is updated.
- Add a 'spec-template.spec.imagePullPolicy: Always' to ensure that the new image is pulled even if it exists in the pod's local cache.
2. Create the Deployment: - Apply the updated YAML file using 'kubectl apply -f redis-deployment.yaml' 3. Verify the Deployment - Check the status of the deployment using 'kubectl get deployments redis-deployment' to confirm the rollout and updated replica count. 4. Trigger the Automatic Update. - Push a new image to the Docker Hub repository 5. Monitor the Deployment: - Use 'kubectl get pods -l app=rediS to monitor the pod updates during the rolling update process. You will observe that one new pod with the updated image is created, and then one old pod is terminated- This ensures that there is no downtime during the update process. 6. Check for Successful Update: - Once the deployment is complete, use 'kubectl describe deployment redis-deployment' to see that the 'updatedReplicaS field matches the 'replicas' field, indicating a successful update.
NEW QUESTION # 235
Context
You must connect to the correct host . Failure to do so may result in a zero score.
[candidate@base] $ ssh ckad00043
A Deployment needs specific RBAC permissions.
Task
First, find the RBAC permissions needed by the scraper Deployment running in the cute-panda namespace .
it kubectl logs may help you to find the permissions it needs.
Next, create a new ServiceAccount named scraper in the namespace cute-panda.
Answer:
Explanation:
See the Explanation below for complete solution.
Explanation:
ssh ckad00043
You have two deliverables here:
* Figure out what RBAC permissions the scraper Deployment needs (the logs will usually show
"Forbidden" with the missing verb/resource).
* Create a ServiceAccount named scraper in namespace cute-panda (and in practice, you then bind the needed permissions to it and use it in the Deployment so it actually works).
Below is the exact CKAD-style workflow.
1) Find the missing RBAC permissions (use logs + events)
1.1 Identify the pods for the Deployment
kubectl -n cute-panda get deploy scraper
kubectl -n cute-panda get pods -l app=scraper 2>/dev/null || kubectl -n cute-panda get pods Pick one pod name and check logs:
kubectl -n cute-panda logs deploy/scraper --tail=100
If the pod is crashlooping and logs are short:
POD=$(kubectl -n cute-panda get pods -o jsonpath='{.items[0].metadata.name}') kubectl -n cute-panda logs "$POD" --previous --tail=200
1.2 Look specifically for "Forbidden" lines
Most apps print errors like:
* ... is forbidden: User "system:serviceaccount:cute-panda:default" cannot list resource "pods" in API group "" in the namespace "cute-panda"
* or cannot get resource "configmaps"...
* or cannot watch ...
If you don't see it in logs, check events:
kubectl -n cute-panda get events --sort-by=.lastTimestamp | tail -n 30
1.3 Extract verb/resource/apiGroup from the error
From a typical Kubernetes RBAC "forbidden" message, capture:
* verb: get/list/watch/create/update/patch/delete
* resource: pods, configmaps, secrets, deployments, etc.
* apiGroup: "" (core), apps, batch, etc.
* namespace: cute-panda (this is a namespaced permission if it's a Role) You may have multiple "cannot ..." lines # you need to allow all of them.
2) Create the ServiceAccount scraper (required by the task)
kubectl -n cute-panda create serviceaccount scraper
kubectl -n cute-panda get sa scraper
3) Create the RBAC objects to grant the needed permissions
The task says "A Deployment needs specific RBAC permissions" - in CKAD, that usually means: Role + RoleBinding (namespaced) bound to your new ServiceAccount.
3.1 Create a Role (template you fill from the log output)
Create scraper-role.yaml:
cat <<'EOF' > scraper-role.yaml
apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
name: scraper-role
namespace: cute-panda
rules:
# EXAMPLE ONLY: replace these rules with what your logs show
- apiGroups: [""]
resources: ["pods"]
verbs: ["get","list","watch"]
EOF
Apply it:
kubectl apply -f scraper-role.yaml
3.2 Bind the Role to the ServiceAccount
kubectl -n cute-panda create rolebinding scraper-rb \
--role=scraper-role \
--serviceaccount=cute-panda:scraper
Verify:
kubectl -n cute-panda get role scraper-role
kubectl -n cute-panda get rolebinding scraper-rb -o yaml
4) Update the Deployment to use the new ServiceAccount (so it actually works) Check current SA (likely default):
kubectl -n cute-panda get deploy scraper -o jsonpath='{.spec.template.spec.serviceAccountName}{"\n"}' Patch it to use scraper:
kubectl -n cute-panda patch deploy scraper -p '{"spec":{"template":{"spec":{"serviceAccountName":" scraper"}}}}' Rollout:
kubectl -n cute-panda rollout status deploy scraper
Re-check logs to confirm RBAC errors are gone:
kubectl -n cute-panda logs deploy/scraper --tail=100
NEW QUESTION # 236
You are running a critical application that requires high availability and minimal downtime during updates. Your current deployment strategy uses a single Deployment with 3 replicas. You need to ensure that during updates, only one pod is unavailable at any given time, minimizing service disruption. Design a deployment strategy that meets this requirement and allows for seamless updates.
Answer:
Explanation:
See the solution below with Step by Step Explanation.
Explanation:
Solution (Step by Step) :
1. Define Rolling Update Strategy:
- In your Deployment configuration, specify the rolling update strategy with 'maxunavailable: 1 s and 'maxSurge: O'. This ensures that during updates, only one pod is taken down at a time, while the remaining two continue serving traffic.
2. Use Liveness and Readiness Probes: - Configure liveness and readiness probes for your application containers. Liveness probes Check tne nealth of running containers and restan them if unhealthy. Readiness probes check if a container is ready to receive traffic. - This ensures that only healthy pods are marked as ready, and traffic is routed only to ready pods.
3. Implement Horizontal Pod Autoscaling (HPA): - Set up HPA to automatically scale the number of pods based on CPU or memory utilization- This ensures that the application can handle increased trattiC during updates without compromising performance. - You can configure the desired minimum and maximum replicas for the HPA based on your application's requirements. 4. Use Service with Session Affinity: - Configure your Service to use 'ClientlP' or 'Cookie' session affinity. This ensures that client connections are consistently routed to the same pod during the rolling update, minimizing disruption for users.
5. Use Daemonsets for System Components: - If you have any system components (like monitoring agents or log collectors) that need to run on every node in the cluster, use DaemonSets instead of Deployments. - DaemonSets ensure that these components are always running on all nodes, even during node restarts or updates, ensuring continuous monitoring and logging.
NEW QUESTION # 237
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 # 238 
Task:
The pod for the Deployment named nosql in the craytisn namespace fails to start because its container runs out of resources.
Update the nosol Deployment so that the Pod:
1) Request 160M of memory for its Container
2) Limits the memory to half the maximum memory constraint set for the crayfah name space.
Answer:
Explanation:
See the solution below.
Explanation:
Solution:
NEW QUESTION # 239
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