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PROFESSIONAL-DATA-ENGINEER · Question #287

PROFESSIONAL-DATA-ENGINEER Question #287: Real Exam Question with Answer & Explanation

B is correct because Cloud Build provides a CI/CD pipeline that can trigger Terraform to provision the GKE infrastructure (including node pools with GPUs, local SSDs, and 8 Gbps networking via specific machine types) and simultaneously deploy containers using the latest images fr

Submitted by renata2k· Mar 30, 2026Building and operationalizing data processing systems

Question

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?

Options

  • AUse Compute Engine startup scripts to pull container images, and use gcloud commands to provision the infrastructure.
  • BUse Cloud Build to schedule a job using Terraform build to provision the infrastructure and launch with the most current container images.
  • CUse GKE to autoscale containers, and use gcloud commands to provision the infrastructure.
  • DUse Dataflow to provision the data pipeline, and use Cloud Scheduler to run the job.

Explanation

B is correct because Cloud Build provides a CI/CD pipeline that can trigger Terraform to provision the GKE infrastructure (including node pools with GPUs, local SSDs, and 8 Gbps networking via specific machine types) and simultaneously deploy containers using the latest images from the registry - all in a single, automated workflow.

A is wrong because Compute Engine startup scripts operate at the VM level, not the container level, and manual gcloud commands are neither efficient nor repeatable for provisioning complex GKE infrastructure.

C is wrong because GKE autoscaling manages workload scaling (pod replicas), not infrastructure provisioning - and again, gcloud commands are manual and not suited for efficiently managing hardware-specific node configurations.

D is wrong because Dataflow is a managed data processing service (Apache Beam), not an infrastructure provisioner, and Cloud Scheduler simply triggers jobs on a schedule - neither addresses GKE node configuration or container registry deployments.

Memory tip: Think "Build to Build" - Cloud Build builds your pipeline, Terraform builds your infrastructure. When you see "efficiently provision + deploy latest images," the answer involves IaC (Terraform) + CI/CD (Cloud Build), not manual gcloud commands or unrelated services like Dataflow.

Topics

#GKE#Infrastructure as Code#CI/CD#Container Deployment

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