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| Section | Objectives |
|---|---|
| Topic 1: Application Environment, Configuration and Security | - Security contexts and service accounts - ConfigMaps and Secrets usage |
| Topic 2: Application Observability and Maintenance | - Understand probes and health checks - Monitor and troubleshoot applications |
| Topic 3: Application Deployment | - Use Kubernetes primitives to implement deployments - Understand rolling updates and rollbacks |
| Topic 4: Services and Networking | - Pod networking concepts - Ingress basics - Services (ClusterIP, NodePort) |
| Topic 5: State Persistence | - PersistentVolumes and PersistentVolumeClaims - Storage classes and volume mounting |
| Topic 6: Application Design and Build | - Understand multi-container Pod design patterns - Understand Jobs and CronJobs - Define, build and modify container images |
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NEW QUESTION # 61
Refer to Exhibit.
Task
Create a new deployment for running.nginx with the following parameters;
* Run the deployment in the kdpd00201 namespace. The namespace has already been created
* Name the deployment frontend and configure with 4 replicas
* Configure the pod with a container image of lfccncf/nginx:1.13.7
* Set an environment variable of NGINX__PORT=8080 and also expose that port for the container above
Answer:
Explanation:
Solution:
NEW QUESTION # 62
Refer to Exhibit.
Set Configuration Context:
[student@node-1] $ | kubectl
Config use-context k8s
Task
You have rolled out a new pod to your infrastructure and now you need to allow it to communicate with the web and storage pods but nothing else. Given the running pod kdsn00201 -newpod edit it to use a network policy that will allow it to send and receive traffic only to and from the web and storage pods.
Answer:
Explanation:
To allow a pod to send and receive traffic only to and from specific pods, you can use network policies in Kubernetes.
First, you will need to create a network policy that defines the allowed traffic. You can create a network policy yaml file with the following rules:
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
name: newpod-network-policy
namespace: default
spec:
podSelector:
matchLabels:
app: kdsn00201-newpod
ingress:
- from:
- podSelector:
matchLabels:
app: web
- podSelector:
matchLabels:
app: storage
This policy will only allow incoming traffic to the pod with the label app=kdsn00201-newpod from pods with the label app=web or app=storage. If you have different labels on your web and storage pods please update the matchLabels accordingly.
Once you have created the network policy, you can apply it to the cluster by running the following command:
kubectl apply -f <network-policy-file>.yaml
This will apply the network policy to the cluster, and the newpod will only be able to send and receive traffic to and from the web and storage pods.
Please note that, NetworkPolicy resource is not available by default, you need to enable the NetworkPolicy feature on your Kubernetes cluster. This feature is enabled by default on some clusters and must be explicitly enabled on others. You can check if NetworkPolicy is available by running the command kubectl api-versions | grep networking Also, you need to ensure that the pods that you want to allow traffic to and from are running on the same namespace.
NEW QUESTION # 63
Context
You must connect to the correct host . Failure to do so may result in a zero score.
!
[candidate@base] $ ssh ckad00028
Task
A Pod within the Deployment named honeybee-deployment and in namespace gorilla is logging errors.
Look at the logs to identify error messages.
Look at the logs to identify error messages.
Find errors, including User
"system:serviceaccount:gorilla:default" cannot list resource "pods" [ ... ] in the namespace "gorilla" Update the Deployment honeybee-deployment to resolve the errors in the logs of the Pod.
The honeybee-deployment 's manifest file can be found at
/home/candidate/prompt-escargot/honey bee-deployment.yaml
Answer:
Explanation:
See the Explanation below for complete solution.
Explanation:
ssh ckad00028
You're seeing RBAC errors like:
User "system:serviceaccount:gorilla:default" cannot list resource "pods" ... in namespace "gorilla" That means the Pod is running as the default ServiceAccount and needs permission to list pods (and possibly also get/watch).
You must fix it by updating the Deployment (via its manifest file) and giving it the proper RBAC.
1) Confirm the error in logs
kubectl -n gorilla get deploy honeybee-deployment
kubectl -n gorilla logs deploy/honeybee-deployment --tail=200
If it's CrashLooping and you need previous logs:
POD=$(kubectl -n gorilla get pods -l app=honeybee -o jsonpath='{.items[0].metadata.name}' 2>/dev/null || kubectl -n gorilla get pods -o jsonpath='{.items[0].metadata.name}') kubectl -n gorilla logs "$POD" --previous --tail=200 You should see the "cannot list resource pods" line.
2) Create a dedicated ServiceAccount for the app
(Using a dedicated SA is standard practice; the task wants you to "resolve the errors".) kubectl -n gorilla create serviceaccount honeybee-sa kubectl -n gorilla get sa honeybee-sa
3) Create RBAC: Role + RoleBinding (namespaced)
This will allow listing pods in namespace gorilla.
cat <<'EOF' > honeybee-rbac.yaml
apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
name: honeybee-pod-reader
namespace: gorilla
rules:
- apiGroups: [""]
resources: ["pods"]
verbs: ["get", "list", "watch"]
---
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
name: honeybee-pod-reader-binding
namespace: gorilla
subjects:
- kind: ServiceAccount
name: honeybee-sa
namespace: gorilla
roleRef:
apiGroup: rbac.authorization.k8s.io
kind: Role
name: honeybee-pod-reader
EOF
Apply it:
kubectl apply -f honeybee-rbac.yaml
Quick verification (optional but very useful):
kubectl auth can-i list pods -n gorilla --as=system:serviceaccount:gorilla:honeybee-sa Should return yes.
4) Update the Deployment manifest to use the new ServiceAccount
The manifest is at:
/home/candidate/prompt-escargot/honey bee-deployment.yaml
Because there's a space in the filename, quote it.
4.1 Edit the file
cd /home/candidate/prompt-escargot
ls -l
vi "honey bee-deployment.yaml"
In the Deployment YAML, add (or set) this under:
spec.template.spec:
serviceAccountName: honeybee-sa
Example location:
spec:
template:
spec:
serviceAccountName: honeybee-sa
containers:
- name: ...
Save and exit.
4.2 Apply the updated manifest
kubectl apply -f "/home/candidate/prompt-escargot/honey bee-deployment.yaml"
5) Ensure rollout succeeds and errors are gone
kubectl -n gorilla rollout status deploy honeybee-deployment
kubectl -n gorilla logs deploy/honeybee-deployment --tail=200
Also confirm the pods now run with the right ServiceAccount:
kubectl -n gorilla get pods -o jsonpath='{range .items[*]}{.metadata.name}{" sa="}{.spec.
serviceAccountName}{"\n"}{end}'
You should no longer see the RBAC "cannot list pods" errors.
NEW QUESTION # 64
Context
Task:
A pod within the Deployment named buffale-deployment and in namespace gorilla is logging errors.
1) Look at the logs identify errors messages.
Find errors, including User "system:serviceaccount:gorilla:default" cannot list resource "deployment" [...] in the namespace "gorilla"
2) Update the Deployment buffalo-deployment to resolve the errors in the logs of the Pod.
The buffalo-deployment 'S manifest can be found at -/prompt/escargot/buffalo-deployment.yaml
Answer:
Explanation:
Solution:
NEW QUESTION # 65
You are building a new web application that utilizes a microservice architecture- One of the microservices, 'recommendation-service', is responsible for providing personalized product recommendations to users.
This service uses a machine learning model for generating recommendations based on user purchase history and browsing behavior. The model is trained offline and its weights are stored in a 'model-store' service.
Design a mufti-container Pod for the 'recommendation-service' that incorporates the following considerations:
- The Pod should include a primary container for the 'recommendation-service' application.
- The Pod should include a secondary container that runs the 'model-store' service to provide access to the trained model weights.
- Both containers should share a common volume to ensure that the model weights are available to the 'recommendation-service' container-
- The recommendation-service' snould be able to access the model weignts from the 'model-store' container witnout relying on a network call to another service-
- The recommendation-service' container should be configured to periodically update the model weights from the 'model-store' container when a new version of the model is available.
Answer:
Explanation:
See the solution below with Step by Step Explanation.
Explanation:
Solution (Step by Step) :
1. Create the Deployment YAML:
- Define a Deployment with the name 'recommendation-service'
- Set the replicas to for redundancy and scalability.
- Specify the labels Sapp: recommendation-service' for selecting the Pods in the Deployment.
- Create a 'template' section to define the Pod specificatiom
2. Deploy the Resources: - Apply the Deployment using 'kubectl apply -f deployment-yamp 3. Verify the Deployment: - Check the status of the Deployment using 'kubectl get deployments recommendation-service and ensure that three Pods are running. 4. Contigure the 'recommendation-service' - Modify the 'recommendation-service application to load the model weights from the specified path ClmodeVIatest-modeI_weightS). - Implement a mechanism within the 'recommendation-service to periodically check tor updated model weights in the shared volume. 5. Configure the 'model-store service: - Ensure that the model-store service is properly configured to store and retrieve the model weights. - Implement a mechanism in the 'model-store' service to notify the 'recommendation-service when a new model version is available. This notification can be achieved using a shared volume or a separate messaging system. 6. Test the Application: - Send requests to the 'recommendation-service' to generate recommendations. - Monitor the 'model-store' service and the shared volume to verify that the model weights are being updated correctly and the recommendation- service' is using the latest model version. Important Considerations: - Ensure that the 'recommendation-service' application is properly configured to access and load the model weights from the shared volume. - Implement a robust model management strategy, including versioning and rollback mechanisms, to ensure that the recommendation-service always uses the appropriate model. - Consider using a dedicated model store service that provides a dedicated API for retrieving and updating model weights. This can simplify the communication between the 'recommendation-service' and the model store. - Monitor the performance and resource usage of both services to ensure optimal performance.,
NEW QUESTION # 66
......
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