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NEW QUESTION # 162
You are running a Deployment for a database service with 3 replicas. You want to ensure that only one pod is updated at a time, but you need to guarantee that the database service remains available throughout tne update process. How would you configure the Deployment to achieve this?
Answer:
Explanation:
See the solution below with Step by Step Explanation.
Explanation:
Solution (Step by Step) :
1. Update the Deployment YAMLI
- Update the 'replicas' to 3.
- Define 'maxiJnavailable: 1 ' and 'maxSurge: O' in the 'strategy-rollingupdate' section to control the rolling update process.
- Use a 'readiness probe' within your container definition to ensure that the pod is considered ready only when tne database is successfully started and connected.
- Configure a 'strategy-type' to 'RollingUpdate' to trigger a rolling update when the deployment is updated.
2. Create the Deployment: - Apply the updated YAML file using 'kubectl apply -f database-deployment-yamp 3. Verify the Deployment - Check the status of the deployment using 'kubectl get deployments database-deployment to confirm the rollout and updated replica count. 4. Trigger the Automatic Update: - Pusn a new image to the Docker Hub repository. 5. Monitor the Deployment - Use 'kubectl get pods -l to monitor the pod updates during the rolling update process. You will observe that only one pod is terminated at a time. The readiness probe will ensure that a new pod is only considered ready when it's successfully connected to the database. 6. Check for Successful Update: - Once the deployment is complete, use 'kubectl describe deployment database-deployment to see that the 'updatedReplicas' field matches the 'replicas' field, indicating a successful update.,
NEW QUESTION # 163
You are asked to prepare a canary deployment for testing a new application release.
You must connect to the correct host . Failure to do so may result in a zero score.
[candidate@base] $ ssh ckad00023
Modify the Deployments so that:
a maximum number of 10 Pods run in the moose namespace.
20% of the chipmunk-service 's traffic goes to the canary-chipmunk-deployment Pod (s).
The Service is exposed on NodePort 30000. To test its load- balancing, run
[candidate@ckad00023]
$ curl http://localhost:30000/
or open this URL in the remote desktop's browser.
Answer:
Explanation:
See the Explanation below for complete solution.
Explanation:
ssh ckad00023
You need two outcomes in moose:
* At most 10 Pods total (across both Deployments).
* About 20% of chipmunk-service traffic goes to canary-chipmunk-deployment.
In Kubernetes Services, traffic distribution is (roughly) proportional to the number of ready endpoints behind the Service. So the standard canary trick is:
* total endpoints = 10
* canary endpoints = 2
* current endpoints = 8That gives ~20% to canary.
1) Inspect what exists
kubectl -n moose get deploy
kubectl -n moose get svc chipmunk-service -o wide
kubectl -n moose describe svc chipmunk-service
Get the Service selector (important):
kubectl -n moose get svc chipmunk-service -o jsonpath='{.spec.selector}{"\n"}' Check current replicas:
kubectl -n moose get deploy current-chipmunk-deployment -o jsonpath='{.spec.replicas}{"\n"}' kubectl -n moose get deploy canary-chipmunk-deployment -o jsonpath='{.spec.replicas}{"\n"}' List pods + labels (to confirm both Deployments' pods match the Service selector):
kubectl -n moose get pods --show-labels
2) Ensure both Deployments are behind the Service
This is the key: the pods from BOTH deployments must match the Service selector.
* If the Service selector is something like app=chipmunk, then both Deployments' pod templates must include app: chipmunk.
* If one Deployment doesn't match, patch its pod template labels to match the selector.
2A) Example: selector is app=chipmunk
(Only do this if you see the Service selector contains app=chipmunk and one of the deployments is missing it.) kubectl -n moose patch deploy current-chipmunk-deployment \
-p '{"spec":{"template":{"metadata":{"labels":{"app":"chipmunk"}}}}}'
kubectl -n moose patch deploy canary-chipmunk-deployment \
-p '{"spec":{"template":{"metadata":{"labels":{"app":"chipmunk"}}}}}'
Wait for rollouts if patches triggered new ReplicaSets:
kubectl -n moose rollout status deploy current-chipmunk-deployment
kubectl -n moose rollout status deploy canary-chipmunk-deployment
Verify endpoints now include pods from both deployments:
kubectl -n moose get endpoints chipmunk-service -o wide
3) Set replicas to enforce "max 10 pods" and "20% canary"
Set:
* current = 8
* canary = 2Total = 10.
kubectl -n moose scale deploy current-chipmunk-deployment --replicas=8
kubectl -n moose scale deploy canary-chipmunk-deployment --replicas=2
Wait until ready:
kubectl -n moose rollout status deploy current-chipmunk-deployment
kubectl -n moose rollout status deploy canary-chipmunk-deployment
Confirm total pods is 10 (or less) and all are Running/Ready:
kubectl -n moose get pods
kubectl -n moose get pods | tail -n +2 | wc -l
Confirm endpoints count matches 10:
kubectl -n moose get endpoints chipmunk-service -o jsonpath='{.subsets[*].addresses[*].ip}' | wc -w
4) Test load balancing via NodePort 30000
Run several times:
for
i in $(seq 1 30); do curl -s http://localhost:30000/; echo; done
You should see canary responses appear roughly ~20% of the time (not exact every run).
If you want a clearer signal, check which pods are endpoints and ensure 2 belong to canary and 8 to current:
kubectl -n moose get pods -l app=chipmunk -o wide
kubectl -n moose get endpoints chipmunk-service -o wide
NEW QUESTION # 164
Refer to 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
Answer:
Explanation:
Solution:
NEW QUESTION # 165 
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 # 166
You are deploying a new application named 'cnat-app' that requires 5 replicas. You want to implement a rolling update strategy that ensures only one pod is unavailable at any given time, while also allowing for the creation of two new pods simultaneously. This will help to ensure that the application remains available during the update process.
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 5.
- Define 'maxunavailable: and 'maxSurge: 2' in the 'strategy.roIIingUpdate' section.
- 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 chat-app-deployment.yamr 3. Verify the Deployment: - Check the status of the deployment using 'kubectl get deployments chat-app-deployment to confirm the rollout and updated replica count. 4. Trigger the Automatic Update: - Push a new image to the 'example/chat-app:latest' Docker Hub repository. 5. Monitor the Deployment - Use ' kubectl get pods -l app=chat-app' to monitor the pod updates during the rolling update process. You will observe that one pod is terminated at a time, while two new pods with the updated image are created- 6. Check for Successful Update: - Once the deployment is complete, use 'kubectl describe deployment chat-app-deployment to see that the 'updatedReplicas' field matches the 'replicas' field, indicating a successful update.
NEW QUESTION # 167
......
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