GCP Professional Cloud Architect Practice Question

Your team of 50 developers maintains dozens of microservices deployed on GKE. Engineering leadership wants to shorten feedback loops and improve code quality by introducing Gemini Code Assist at both development time and in continuous integration (CI). All proprietary source code and model prompts must stay inside the company's Google Cloud project, and no workload may rely on external SaaS providers or be run in non-compliant regions. Which approach best satisfies these requirements?

Export the full repository each night to an external SaaS that uses a GPT-4 engine for automated code reviews, then manually merge the suggested patches.
Run Cloud Workstations with the Cloud Code plugin enabled for Gemini Code Assist, and add a scripted Gemini-powered step to Cloud Build that executes under the project's default Cloud Build service account.
Insert a Cloud Function in the pipeline that sends container images to an external generative-AI API for summarization before pushing them to Artifact Registry.
Deploy a custom fine-tuned LLM on Compute Engine instances located in a lower-cost region that does not meet the organization's compliance standards, replacing existing static analysis.

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Answer Description

Running Cloud Workstations with the Cloud Code plugin lets developers access Gemini Code Assist for real-time completions and inline security recommendations while editing, without moving code outside the project. Adding a dedicated Cloud Build step that programmatically invokes the same Gemini model under the default Cloud Build service account lets the team prompt the model (for example, to scaffold unit tests or suggest refactors) during CI; all artifacts remain inside the project, and no third-party SaaS or out-of-region service is used. The other approaches either export code to external providers, run in regions that violate compliance, or transmit artifacts outside the project boundary, contradicting the stated constraints.

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