Free Google Professional Data Engineer Actual Exam Questions - Question 3 Discussion
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?
Not B, autoscaling usually manages pods, not the underlying specialized nodes with GPUs and local SSDs, so it might not guarantee the exact hardware specs needed for each node.
It’s C. Using Cloud Build with Terraform lets you manage both infrastructure and deployment as code, which fits the need to have GPUs, local SSDs, and specific bandwidth guaranteed on the nodes. This approach also ensures you always launch containers with the latest configurations since Cloud Build can trigger builds and deployments automatically. Options A and B don’t clearly handle the infrastructure setup for GPUs and local SSDs as well, while D is off because Dataflow isn’t designed for managing GKE clusters or container deployment. So C covers everything more cleanly.
Maybe C makes more sense since Terraform can handle the GPU and local SSD setup, and Cloud Build can automate pushing the latest container images into the deployment. Seems more streamlined than just relying on autoscaling.
B tbh. Using GKE autoscaling sounds like the straightforward way to handle container deployment efficiently, plus you get the autoscale benefits out of the box. The question mentions needing GPUs and local SSDs, so provisioning with gcloud commands alongside GKE makes sense to tailor the nodes well. Cloud Build and Terraform in C complicate things more than needed if the goal is just spinning up a GKE cluster with the right specs and keeping containers updated. D is clearly off since Dataflow is for streaming pipelines, not managing Kubernetes infrastructure. A feels too manual with startup sc
C/B? Using Cloud Build with Terraform (C) sounds solid for automating infra and deployment, but I’m not totally sure if Cloud Build alone handles the actual image updates on GKE nodes efficiently. Option B uses GKE autoscaling, which seems natural for scaling containers, though it doesn’t explicitly mention managing the GPU and local SSD requirements. Still, B feels more native to GKE management without extra layers. A feels outdated since startup scripts and gloud commands can get messy for ongoing management. D is off since Dataflow isn’t really meant for GKE or GPU provisioning.
Maybe C makes sense since Terraform can handle infrastructure and Cloud Build can trigger container deployments, so you get automated updates with the latest images. D sounds more like streaming data, not container orchestration.
C – does it say which Terraform version to use or any specific container registry?