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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Application Environment, Configuration and Security | 25% | - Discover and use resources that extend Kubernetes (CRD, Operators) - SecurityContexts - Understanding and defining resource requirements, limits and quotas - ServiceAccounts - ConfigMaps and Secrets - Understand authentication, authorization and admission control |
| Topic 2: Application Deployment | 20% | - Understand Deployments and how to perform rolling updates - Kustomize - Use Kubernetes primitives to implement common deployment strategies (e.g., blue/green or canary) - Use the Helm package manager to deploy existing packages |
| Topic 3: Application Observability and Maintenance | 15% | - Use built-in CLI tools to monitor Kubernetes applications - Implement probes and health checks - Understand API deprecation policies - Utilize container logs - Debugging in Kubernetes |
| Topic 4: Application Design and Build | 20% | - Choose and use the right workload resource (Deployment, DaemonSet, CronJob, etc.) - Define, build and modify container images - Utilize persistent and ephemeral volumes - Understand multi-container Pod design patterns (e.g., sidecar, init and others) |
| Topic 5: Services and Networking | 20% | - Provide and troubleshoot access to applications via services - Use Ingress rules to expose applications - Demonstrate basic understanding of NetworkPolicies |
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NEW QUESTION # 102
You have a Deployment running with a specific image tag, and you want to roll out a new version with a different image tag- However, you want to ensure that the update process is gradual, and only one pod is updated at a time. Additional'" you need to monitor the performance metrics of the application during the update, and if the performance degrades significantly, you need to rollback to the previous version How would you implement this using Kustomize and other Kubernetes features?
Answer:
Explanation:
See the solution below with Step by Step Explanation.
Explanation:
Solution (Step by Step) :
1. Create a customization file:
resources :
- deployment. yaml
2. Create a deployment-yaml file:
3. Configure a rolling update strategy: - Edit the 'deployment.yamr file and add the following to the 'spec-strategy' section:
4. Set up monitoring with Prometheus and Grafana: - Install Prometheus and Grafana on your Kubernetes cluster. - Configure Prometheus to scrape metrics from your application pods. - Create Grafana dashboards to visualize the relevant metrics. 5. Create an alert in Prometheus: - Define an alert that triggers if the application's performance degrades significantly - This alert should be configured to send notifications to your team. 6. Create a rollback mechanism: - IJse a script or a tool like 'oubect1 rollout undo' to rollback the deployment to the previous version if the performance alert is triggered. 7. Update the deployment with the new image tag: - Edit the 'deployment-yamp file and change the 'image' to 'example/nginx:v2 8. Apply the changes to your Kubernetes cluster: bash oubect1 apply -f deployment-yaml - The 'maxSurge' and 'maxunavailable' settings in the 'rollinglJpdate' strategy control the maximum number of pods that can be added or removed during the update process. - Prometheus and Grafana provide a way to monitor the performance metrics of your application. - The Prometheus alert helps you identify if the performance degrades significantly during the update process. - The rollback mechanism allows you to revert to the previous version if the performance alert is triggered. - This setup ensures a gradual update process and provides a mechanism to mitigate potential performance issues. ,
NEW QUESTION # 103
You are tasked with deploying a complex application using Helm. The application consists of multiple microservices, each with its own deployment and service. To simplify the deployment and management of these microservices, you need to implement a mecnanism that allows you to automatically create and manage namespaces based on the name of the Helm release.
Answer:
Explanation:
See the solution below with Step by Step Explanation.
Explanation:
Solution (Step by Step) :
1. Create a Custom Helm Chart:
- Begin by creating a custom Helm chart named 'my-app-chart' to manage the application's multiple microservices.
2. Implement a Namespace Creation Function:
- Within the 'my-app-chafltemplatesr directory, create a file named 'namespace-yamr and define the namespace creation function.
- This function uses the Helm release name to dynamically generate a namespace with the format '-namespace' 3. Add the Namespace to the Chan: - Modify the 'my-app-chart/templates/service.yamr and 'my-app-chart/templates/deployment_yamr for each microservice to ensure the deployments and services reside within the dynamically created namespace:
4. Deploy the Chart with Different Releases: - Use tne following command to deploy tne chart with different releases, each creating a separate namespace: bash nelm install release1 my-app-chart helm install release2 my-app-chart - This will create namespaces release1-namespace' and release2-namespace , each containing the deployments and services of the respective releases. 5. Manage and Clean Up: - To manage and clean up the deployments and namespaces, you can use regular Helm commands within the context or each namespace: bash kubectl --namespace release1 -namespace get pods helm delete release1 kubectl delete namespace release1-namespace - This approach provides a structured and automated method for managing multiple microservices within separate namespaces using Helm releases.,
NEW QUESTION # 104
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: B
NEW QUESTION # 105
You have a Deployment named 'my-app-deployment running a Flask application. You want to add a liveness probe that checks if the Flask application is responding on port '5000' and a readiness probe that checks if the application is ready to receive requests. Implement these probes using Kustomize.
Answer:
Explanation:
See the solution below with Step by Step Explanation.
Explanation:
Solution (Step by Step) :
1. Create a base Deployment configuration:
2. Create a 'kustomization.yamr file:
3. Create 'patcnes/liveness-probe.yaml':
4. Create 'patches/readiness-probe-yaml':
5. Apply the Kustomize configuration: bash kustomize . I kubectl apply -t- - Liveness probe: This probe checks if the application is still alive and running. It uses a TCP socket to connect to port ' 5000' and waits for 15 seconds before making the first cneck. It checks every 20 seconds, and if it fails 3 times in a row, the pod is restarted. - Readiness probe: This probe checks if the application is ready to receive requests. It also uses a TCP socket to connect to port '5000'. It checks every 10 seconds and waits for 5 seconds before the first check. If it fails 2 times in a row, the pod is marked as unhealthy and excluded trom receiving traffic. Note: Make sure your Flask application is actually listening on port '5000' and responding to requests. ,
NEW QUESTION # 106
Context
Task:
Create a Pod named nginx resources in the existing pod resources namespace.
Specify a single container using nginx:stable image.
Specify a resource request of 300m cpus and 1G1 of memory for the Pod's container.
Answer:
Explanation:
Solution:
NEW QUESTION # 107
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