GCP Professional Cloud Architect Practice Question

Your development organization is modernizing its SDLC on Google Cloud. Architects want to reduce lead time for new micro-service features, improve test coverage, and decrease the number of post-release defects found in production. They are evaluating how Gemini for Google Cloud could be embedded into existing tooling without introducing new third-party products or changing the deployment target (services are built in Cloud Build and deployed to Cloud Run through Cloud Deploy). Which approach best aligns with these goals while keeping the current tool chain intact?

  • Insert a mandatory Gemini chat review after Cloud Deploy promotions so SREs can ask natural-language questions about release artifacts before approving production rollout.

  • Replace Cloud Build with a third-party SaaS CI service that offers an embedded generative AI agent for pull-request reviews and merges back to Cloud Source Repositories.

  • Add a Cloud Functions step that calls the Gemini API after each build to refactor source code and push the result to a new branch that triggers an additional Cloud Deploy stage.

  • Enable Gemini Code Assist in the Cloud Code IDE extensions used by the team so developers receive AI-generated code suggestions, unit-test stubs, and Cloud Build YAML snippets directly within their current repositories before committing.

GCP Professional Cloud Architect
Analyzing and optimizing technical and business processes
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