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GCP Professional Cloud Architect Practice Question

Your company processes protected health information on Google Cloud. Compliance requires that trained models and online prediction requests never traverse the public internet and that no other Google Cloud project can access them. A Cloud Run service in the same project will invoke the model for real-time inference. What architecture best meets these requirements while following the principle of least privilege?

  • Expose the Vertex AI endpoint through Cloud Endpoints behind an internal HTTP(S) load balancer and protect it with an API key; store model artifacts in a private BigQuery dataset with row-level security.

  • Create a VPC Service Controls perimeter that includes the project. Deploy the model to a Vertex AI endpoint configured with a Private Service Connect network and disable public access. Grant the Cloud Run service account only the Vertex AI predict permission and call the endpoint over the PSC internal address.

  • Enable Cloud NAT for the VPC and configure firewall rules to block all egress except to the public Vertex AI predict service; authenticate from Cloud Run using a service account key stored in Secret Manager.

  • Keep the default public Vertex AI endpoint, secure it with Cloud Armor rules that allow traffic only from Cloud Run egress IPs, and store the model in a multi-region Cloud Storage bucket with uniform bucket-level access.

GCP Professional Cloud Architect
Designing for security and compliance
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