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.
Gemini Code Assist is natively integrated with Cloud Code plug-ins for IDEs such as VS Code and JetBrains, where it can generate unit tests, suggest code completions, and create Cloud Build or Cloud Deploy configuration snippets. Using these capabilities inside the developers' existing IDEs improves test coverage and release quality early in the SDLC without altering the established Cloud Build → Artifact Registry → Cloud Deploy pipeline. The other options either require inserting manual review gates that slow lead time, adding third-party SaaS products, or depend on Cloud Functions rather than the existing Cloud Deploy path, so they do not satisfy the stated constraints.
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What is Gemini Code Assist and how does it work?
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How does integrating Gemini Code Assist into Cloud Code maintain the existing tool chain?
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Why is using Cloud Functions or third-party SaaS CI services not ideal for this scenario?
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What is Gemini Code Assist and how does it integrate with Cloud Code?
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What is the SDLC and how is it modernized in Google Cloud?
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How does Cloud Build, Cloud Run, and Cloud Deploy work together in the pipeline?
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