A video streaming platform is preparing to launch a new automated encoding pipeline on Google Cloud. The infrastructure consists of private Google Kubernetes Engine (GKE) clusters and compute node pools managed using Terraform. To validate the technical solution prior to full production deployment, the cloud architecture team must establish a pre-deployment testing procedure that validates infrastructure automated provisioning, security boundaries, and capacity limits. Which testing and validation procedure should the Lead Cloud Architect recommend?
- Pre-request necessary regional compute quotas, store Terraform state in Cloud Storage with object versioning enabled, and restrict GKE control plane access to authorized internal IP ranges during deployment validation.Answer
- BExecute large-scale load tests directly against the newly provisioned staging environment without verifying regional compute quota limits, assuming default project quotas dynamically scale.
- CProvision the test clusters using automated CI/CD runners while saving the Terraform state file in the local runner workspace to accelerate testing pipeline execution time.
- DGrant the deployment service account the Service Account Admin role across all testing projects so compute instances can manage and assume worker roles dynamically.
Answer
Pre-request necessary regional compute quotas, store Terraform state in Cloud Storage with object versioning enabled, and restrict GKE control plane access to authorized internal IP ranges during deployment validation.
The correct answer addresses all validation pillars for testing technical solutions on GCP: ensuring capacity quota availability prior to testing, securing IaC state management in remote versioned storage, and enforcing control plane access perimeters on private GKE clusters.
Step-by-Step Solution
Key Concept
Technical solution pre-deployment validation procedures covering infrastructure quota readiness, IaC state integrity, and endpoint security controls.