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| Section | Weight | Objectives |
|---|---|---|
| Application Observability and Maintenance | 15% | - Monitor applications using CLI tools - Debug applications in Kubernetes - Understand API deprecations - Work with container logs - Implement probes and health checks |
| Application Design and Build | 20% | - Understand multi-container Pod design patterns - Choose and use appropriate workload resources - Define, build and modify container images - Utilize persistent and ephemeral volumes |
| Services and Networking | 20% | - Use Ingress rules - Understand and apply NetworkPolicies - Expose applications via Services - Troubleshoot network access |
| Application Environment, Configuration and Security | 25% | - Use ServiceAccounts - Understand authentication, authorization and admission control - Work with ConfigMaps - Configure resource requirements, limits and quotas - Create and consume Secrets - Use custom resources and extensions - Apply application security settings |
| Application Deployment | 20% | - Manage Deployments, rolling updates and rollbacks - Use Helm package manager - Work with Kustomize - Implement deployment strategies |
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NEW QUESTION # 70 
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:
See the solution below.
Explanation
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
name: internal-policy
namespace: default
spec:
podSelector:
matchLabels:
name: internal
policyTypes:
- Egress
- Ingress
ingress:
- {}
egress:
- to:
- podSelector:
matchLabels:
name: mysql
ports:
- protocol: TCP
port: 3306
- to:
- podSelector:
matchLabels:
name: payroll
ports:
- protocol: TCP
port: 8080
- ports:
- port: 53
protocol: UDP
- port: 53
protocol: TCP
NEW QUESTION # 71
You are working on a Kubernetes application that requires a scheduled job to run a data processing script every day at midnight. The script takes approximately 30 minutes to complete and requires access to a persistent volume to store its output dat a. How would you create a Job resource that meets these requirements?
Answer:
Explanation:
See the solution below with Step by Step Explanation.
Explanation:
Solution (Step by Step) :
I). Create a Persistent Volume Claim:
- Define a Persistent Volume Claim (PVC) to request the necessary storage space.
- Specify the access mode and storage class according to your cluster configuration.
2. Define the Job Resource: - Create a Job resource With a 'cronJob' schedule to trigger the execution at midnight every day. - Specify the 'backoffLimit' to control the number of retries in case of failures. - Define the 'spec-template.spec.containers' section for the container running the data processing script. - Mount the PVC to the container using 'volumeMounts'.
3. Create the Job: - Apply the Job YAML file using 'kubectl apply -f data-processing-job.yamr 4. Verify Job Execution: - Use 'kubectl get jobS to monitor the status of the Job. - Check the 'status. completionTime' to verify that the Job completed successfully. - Verity that the output data is stored in the mounted persistent volume. 5. Update the Script - Update the 'your-data-processing-script.sh" with the necessary commands to process the data and store the output in the "ldata' directory. 6. Monitor the Job: - Continuously monitor the Job's status and logs using 'kubectl logs' to ensure it runs correctly. Note: Replace 'your-image-namelatest and 'your-data-processing-scriptsh' with the actual image name and script file respectively,
NEW QUESTION # 72
You have a microservice that is deployed in a Kubernetes cluster, and you want to monitor its performance and health using Prometheus and Grafan a. How can you configure Prometneus to scrape metrics from your microservice and create dashboards in Grafana?
Answer:
Explanation:
See the solution below with Step by Step Explanation.
Explanation:
Solution (Step by Step) :
1. Enable Metrics in Your Microservice:
- Ensure your microservice exposes metrics through an HTTP endpoint using a library like Prometheus Client (for Java), Go metrics, or StatsD.
- Define metrics such as request count, latency, error rate, and other relevant performance indicators.
2. Deploy Prometheus:
- Deploy Prometheus using a 'Deployment and a 'Service'
- Configure Prometheus to scrape metrics trom the microservice by adding its endpoint to the 'scrape_configs' in the 'prometheus.yaml' file.
3. Create a Service for Prometheus to Access the Microservice: - Create a 'Service' of type 'ClusterlP' that exposes the microservice's metrics endpoint (usually port 9100). - Ensure Prometheus can reacn this service. 4. Deploy Grafana: - Deploy Gratana using a 'Deployment' and a 'Service' - Configure Grafana to connect to Prometheus as a data source. 5. Create Dashboards in Grafana: - Use Grafana's dashboard builder to create custom dashboards that visualize the metrics collected by Prometheus. - Add panels to display graphs, charts, and tables that show the performance and health ot your microservice. 6. Configure Alerts in Grafana: - Configure alerts in Grafana based on specific metrics and thresholds. - Set up notifications to alert you when critical issues arise with the microservice. Note: This approach provides comprehensive monitoring for your microservice. Prometheus scrapes metrics from the microservice, stores them in its time series database, and Grafana visualizes these metrics and provides alerts for potential issues. Example Prometheus Scrape Configuration:
Example Grafana Dashboard: - Create a dashboard with panels that show the following metrics: - Request count per minute - Average request latency - Error rate - CPU and memory usage of the microservice container - Set up alerts to notify you it: - The request count exceeds a certain threshold - The average latency exceeds a certain threshold - The error rate exceeds a certain threshold - The CPU or memory usage exceeds a certain threshold,
NEW QUESTION # 73
You need to configure a Kubernetes deployment to use a secret stored in a different namespace. How can you access the secret in a different namespace, and how can you mount it as a file in your deployment's container?
Answer:
Explanation:
See the solution below with Step by Step Explanation.
Explanation:
Solution (Step by Step) :
I). Ensure Access to the Secret:
- The service account used by your deployment needs to have read access to the secret in the other namespace. This can be done using a Role and RoleBinding. If the service account already has access, skip to step 2.
- Create a role in the secret's namespace:
- Create a RoleBinding in the secret's namespace:
- Apply the Role and RoleBinding using: bash kubectl apply -f role-yaml kubectl apply -f rolebinding.yaml 2. Modify your Deployment - Update your Deployment YAML file to mount the secret as a file, specifying the namespace:
- Replace 'my-secret with the actual name of the secret and 'secret-namespace with the namespace where the secret is stored. 3. Apply the Updated Deployment: - Apply the updated deployment using: bash kubectl apply -f my-deployment.yaml 4. Access Secret Data: - The secret's data is now mounted in the container at the specified 'mountPatm. You can access the secret's data using the mounted file.]
NEW QUESTION # 74
You are building a container image for a Spring Boot application that connects to a MySQL database. The application requires specific environment variables, such as the database nostname, username, password, and port. How would you define these environment variables Within the Docker-file to ensure the application runs correctly in a Kubernetes cluster?
Answer:
Explanation:
See the solution below with Step by Step Explanation.
Explanation:
1. Define Environment Variables in Docker-file:
- Utilize the 'ENV' instruction within your Dockerfile to set the necessary environment variables.
- These variables will be accessible to your Spring Boot application during runtime.
- Example:
dockerfile
2. Build the Docker Image: - Construct your Docker image using the Docker-file. - Run the following command: 'docker build -t your-image-name 3. Deploy to Kubernetes: - Create a Deployment or Pod in Kubernetes that utilizes your built image. - Ensure the pod's environment variables align with the ones defined in your Dockerfile. - Example (Deployment YAML):
4. Verify Application Functionality: - Access your deployed application in the Kubernetes cluster. - Verify that it connects successfully to the database and operates as expected.
NEW QUESTION # 75
......
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