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| Section | Objectives |
|---|---|
| Topic 1: Application Observability and Maintenance | - Understand probes and health checks - Monitor and troubleshoot applications |
| Topic 2: Services and Networking | - Ingress basics - Services (ClusterIP, NodePort) - Pod networking concepts |
| Topic 3: Application Environment, Configuration and Security | - ConfigMaps and Secrets usage - Security contexts and service accounts |
| Topic 4: Application Design and Build | - Understand Jobs and CronJobs - Understand multi-container Pod design patterns - Define, build and modify container images |
| Topic 5: State Persistence | - PersistentVolumes and PersistentVolumeClaims - Storage classes and volume mounting |
| Topic 6: Application Deployment | - Understand rolling updates and rollbacks - Use Kubernetes primitives to implement deployments |
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NEW QUESTION # 122
Refer to Exhibit.
Context
A project that you are working on has a requirement for persistent data to be available.
Task
To facilitate this, perform the following tasks:
* Create a file on node sk8s-node-0 at /opt/KDSP00101/data/index.html with the content Acct=Finance
* Create a PersistentVolume named task-pv-volume using hostPath and allocate 1Gi to it, specifying that the volume is at /opt/KDSP00101/data on the cluster's node. The configuration should specify the access mode of ReadWriteOnce . It should define the StorageClass name exam for the PersistentVolume , which will be used to bind PersistentVolumeClaim requests to this PersistenetVolume.
* Create a PefsissentVolumeClaim named task-pv-claim that requests a volume of at least 100Mi and specifies an access mode of ReadWriteOnce
* Create a pod that uses the PersistentVolmeClaim as a volume with a label app: my-storage-app mounting the resulting volume to a mountPath /usr/share/nginx/html inside the pod
Answer:
Explanation:
Solution:
NEW QUESTION # 123
You have a container image for your application that includes both the application code and its dependencies. However, you've noticed that the image size is becoming increasingly large. How would you optimize tne container image to reduce its size and improve deployment efficiency?
Answer:
Explanation:
See the solution below with Step by Step Explanation.
Explanation:
Solution (Step by Step) :
1. Identify and remove unnecessary files: Review the contents ot the image to identify any files that are not required at runtime. This may include development tools, build scripts, documentation, or temporary files. I-Jse a tool like 'docker history' to see the layers of the image and identify unnecessary additions.
2. Optimize build steps: Analyze your Dockerfile and identify any unnecessary commands or layers that contribute to image size. For instance, using multi-stage builds to separate build dependencies from runtime dependencies can significantly reduce image size.
3. Use smaller base images: Choose a leaner base image like 'alpine' or 'scratch' (for minimal environments) instead of a large, bloated base image like 'ubuntu' or 'centos'. Smaller base images offer a significant advantage in terms ot image size-
4. Compress files: Compress static assets, such as configuration files or log files, using tools like 'gzip' or 'bzip2 to reduce their size.
5. Employ a package manager for dependencies: Utilize a package manager like 'apt-gets or 'yum' to install necessary libraries and dependencies. This helps streamline the installation process and optimize package selection.
Example:
Original Dockefflle:
FROM ubuntu:latest
# Install dependencies
RUN apt-get update && \
apt-get install -y python3 python3-pip
# Copy application code and dependencies
COPY - /app
# Run application
CMD ["pytnon3", "/app/app.py"]
Optimized Dockerfile with multi-stage build:
FROM python:3.9-alpine AS builder
# Install dependencies
COPY requirements.txt lapp,/
RUN pip install -r /app/requirements.txt
# Build the application
COPY . /app
RUN python setup.py build
FROM scratch AS runtime
# Copy the compiled application
COPY --from-builder /app/build /app
# Run the application
CMD ["/app/app"]
This optimized Dockerfile uses a smaller base image ('pytnon.3.9-alpineS), leverages multi-stage builds to separate build dependencies from runtime dependencies, and copies only the necessary compiled application to the final image. This results in a significantly smaller container image., You nave a critical batch job tnat processes large amounts of data daily. The job needs to run at a specific time every day, even if the Kubernetes cluster is restarted. Explain how you would design and implement this job using Kubernetes Jobs and CronJobs to ensure reliable execution.
NEW QUESTION # 124
You are building a container image for a Python application that requires a specific version of the 'requests' library. Explain how you would incorporate the 'requests' library into your Dockerfile and ensure that the application can access and use it within the container.
Answer:
Explanation:
See the solution below with Step by Step Explanation.
Explanation:
Solution (Step by Step) :
1. Install the 'requests library in the Dockerfile:
- Use the 'RUN' instruction in your Dockerfile to install the library.
- Utilize the 'pip' package manager to install the specific version of requests required by your application.
- Replace with the desired Python base image. - Ensure that the 'requirements-txt file contains the required dependency, specifically 'requests' and its version. - Include the 'COPY' commands to transfer your application code and other files to the container 2. Import and use the 'requests' library in your Python application: - In your Python application code Capp.pys in this example), impon the 'requests' library. - Use the imported library functions to make HTTP requests as needed in your application logic.
3. Build the Docker image: - Execute the 'docker build' command in your terminal, specifying the Dockerfile location and the image tag. docker build -t my-python-app . 4. Run the container: - Use the 'docker run' command to launch the container, providing the image name. docker run -it my-python-app - The container will run your Python application, and the 'requests' library will be available for use within the container environment.
NEW QUESTION # 125
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 # 126
You nave a microservice tnat iS constantly updated With new features and bug fixes. You want to deploy new versions of this service in a way that minimizes downtime and avoids disrupting the existing application. Explain how you can use Kubernetes features to achieve this goal.
Answer:
Explanation:
See the solution below with Step by Step Explanation.
Explanation:
Solution (Step by Step) :
1. Use a Deployment:
- Create a 'Deployment' object to manage your microservice.
- Specify the desired number of replicas and the container image.
- Use a 'rollinglJpdate' strategy to control the update process.
2. Control Rollout Pace: - Use the maxSurge' and 'maxlJnavailable' parameters to control the number of pods that can be unavailable or created during the update process. - For example, 'maxi-Inavailable: ensures that no pods are unavailable during tne update, while 'maxSurge: 1' allows for one extra pod to be created during the update. 3. Trigger Automatic Updates: - Use a 'Deployment's 'spec-template-spec-imagePullPolicy' to trigger updates automatically when a new image is available. - Set imagePullPolicy: Always' to force a pull of the image each time the deployment is updated. 4. Monitor Rollout Progress: - Use kubectl get pods -l app=my-microservice' to monitor the rollout progress. - You can also use tne 'kubectl rollout status deployment/my-microservice' command to get detailed information about the rollout. 5. Use Liveness and Readiness Probes: - Define liveness and readiness probes to ensure that containers are healthy and ready to serve traffic. - The 'Deployment' will automatically restart unhealthy containers, while readiness probes will ensure that new pods are only considered ready for traffic once they are healtny. 6. Traffic Routing: - Use a 'Service' to expose your 'Deployment' to external clients. - Configure the 'Service' to use a 'LoadBalancer' or 'NodePort' to make the service accessible from outside the cluster. Note: This approach ensures a smooth rollout with minimal downtime. New pods are created with the new image, and traffic is gradually shifted to the updated pods while the old pods are gracefully terminated. The liveness and readiness probes ensure that only healthy pods receive traffic, and the automatic updates triggered by 'imagePullPolicy: Always' keep the service up-to-date.
NEW QUESTION # 127
......
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