You have a data processing application that runs on Google Kubernetes Engine (GKE). Containers need to be launched with their latest available configurations from a container registry. Your GKE nodes need to have GPUs, local SSDs, and 8 Gbps bandwidth. You want to efficiently provision the data processing infrastructure and manage the deployment process. What should you do?
To efficiently provision the data processing infrastructure on Google Kubernetes Engine (GKE) and manage the deployment process, the ideal solution involves using Cloud Build in combination with Terraform. Cloud Build allows for the automation of building, testing, and deployment of applications using Docker containers. Terraform provides Infrastructure as Code (IaC), which is crucial for provisioning GKE clusters with required specifications like GPUs, SSDs, and specific bandwidth. By configuring Terraform to pull the latest container images and using Cloud Build triggers, this setup ensures that the application is always launched with the most current configurations and the infrastructure is managed efficiently. This method also facilitates a Continuous Integration/Continuous Deployment (CI/CD) pipeline, automating the deployment process in a way that gcloud commands alone cannot achieve.
B is the best option to efficiently provision and manage the deployment process for this data processing application on GKE:
• Cloud Build allows you to automate the building, testing, and deployment of your application using Docker containers. • Using Terraform with Cloud Build provides Infrastructure as Code capabilities to provision the GKE cluster with GPUs, SSDs, and network bandwidth. • Terraform can be configured to pull the latest container images from the registry when deploying. • Cloud Build triggers provide event-based automation to rebuild and redeploy when container images are updated. • This provides an automated CI/CD pipeline to launch the application on GKE using the desired infrastructure and latest images. • Dataflow and Cloud Scheduler don't directly provide infrastructure provisioning or deployment orchestration for GKE. • gcloud commands can be used but don't provide the same automation benefits as Cloud Build + Terraform.
So using Cloud Build with Terraform templates provides the most efficient way to provision and deploy this data processing application on GKE.
Maybe B ref: https://cloud.google.com/architecture/managing-infrastructure-as-code
B is the answer.
B is correct
- Dataflow is a fully managed service for stream and batch data processing and is well-suited for real-time data processing tasks like identifying longtail and outlier data points. - Using BigQuery as a sink allows to efficiently store the cleansed and processed data for further analysis and serving it to AI models.
C is correct answer
Sorry I meant B
B. Use Cloud Build to schedule a job using Terraform build to provision the infrastructure and launch with the most current container images.
b is ok
B is correct
I don't really like B or C... but given the choices I would go with B. B-Use Cloud Build to schedule a job using Terraform build to provision the infrastructure and launch with the most current container images. {The Terraform command is Terraform Apply and not Terraform build, but also why not use gcloud container command instead of introducing 3rd party builder image?)... I don't like this choice but it is the best one. C. Use GKE to autoscale containers, and use gcloud commands to provision the infrastructure. {This doesn't handle the building of the infra, or the deployment of the latest images, this one is clearly wrong, not sure why it is marked as the right choice}
another wrong answer?