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GCP Professional Cloud Architect Practice Question

Your company wants to launch a chat-based help-desk assistant within two weeks. The backend runs on Cloud Run and must call the assistant through a private REST endpoint. The team has no capacity to train or maintain ML serving infrastructure but wants the option to later refine the model with proprietary support-chat logs without changing application code. Which approach best meets these requirements with minimal operational overhead?

  • Download the model artifacts from Model Garden to Cloud Storage, build a custom serving container, and deploy it on a GKE Autopilot cluster behind an Internal HTTP(S) Load Balancer; fine-tune by retraining the model and rebuilding the container.

  • Package the model code inside a Cloud Function that serves HTTP requests; when fine-tuning is required, upload new model weights to Cloud Storage and redeploy the function with updated code.

  • Use Vertex AI Model Garden to select a suitable foundation model, click "Deploy to Vertex AI" to create a managed prediction endpoint, invoke it from Cloud Run via Private Service Connect, and later apply Vertex AI tuning to generate a new model version that can be redeployed on the same endpoint.

  • Deploy an open-source LLM from Cloud Marketplace on a managed instance group in Compute Engine and expose it through an internal TCP/UDP Load Balancer; perform future fine-tuning by running custom training jobs on separate GPU VMs.

GCP Professional Cloud Architect
Managing and provisioning a solution infrastructure
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