Pause the rollout of the deployment

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Create a Pod nginx and specify both CPU, memory requests and limits together and verify.

• A.kubectl run nginx-request --image=nginx --restart=Always --dryrun -o yaml > nginx-request.yml
  // add the resources section and create
  apiVersion: v1
  kind: Pod
  metadata:
  labels:
  run: nginx
  name: nginx
  resources:
  requests:
  memory: "100Mi"
  cpu: "0.4"
  limits:
  memory: "200Mi"
  cpu: "7"
  restartPolicy: Always
  k kubectl apply -f nginx-request.yaml
  // Verify
  Kubectl top po
• B.kubectl run nginx-request --image=nginx --restart=Always --dryrun -o yaml > nginx-request.yml
  // add the resources section and create
  apiVersion: v1
  kind: Pod
  metadata:
  labels:
  run: nginx
  name: nginx-request
  spec:
  containers:
  - image: nginx
  name: nginx
  resources:
  requests:
  memory: "100Mi"
  cpu: "0.5"
  limits:
  memory: "200Mi"
  cpu: "1"
  restartPolicy: Always
  k kubectl apply -f nginx-request.yaml
  // Verify
  Kubectl top po

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Score: 4%

Task
Create a persistent volume with name app-data , of capacity 1Gi and access mode ReadOnlyMany. The type of volume is hostPath and its location is /srv/app-data .

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Change the Image version back to 1.17.1 for the pod you just updated and observe the changes

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Create a deployment spec file that will:
* Launch 7 replicas of the nginx Image with the labelapp_runtime_stage=dev
* deployment name: kual00201
Save a copy of this spec file to /opt/KUAL00201/spec_deployment.yaml
(or /opt/KUAL00201/spec_deployment.json).
When you are done, clean up (delete) any new Kubernetes API object that you produced during this task.

Correct Answer:

See the solution below.
Explanation
solution

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