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GCP Professional Cloud Architect Practice Question

A global electronics manufacturer is migrating its vision-based defect-detection application to Google Cloud. The software is built with Cloud Build, container images are stored in Artifact Registry, and Cloud Deploy pushes releases to a multi-regional Google Kubernetes Engine (GKE) production fleet. Under the company's ISO 9001 quality-management system, every new release must automatically demonstrate at least 99 % defect-detection precision before it can be promoted to production, and the test evidence must be retained for future audits. Which strategy best satisfies these quality-control obligations while minimizing manual effort?

  • Add a Cloud Deploy verification gate that triggers a Cloud Functions job to run benchmark tests against a curated image set; have the function write results to Cloud Monitoring and fail the rollout if precision is below 99 %. Store the signed container image and its provenance in Artifact Registry for audit traceability.

  • During Cloud Build, print model-accuracy statistics to the build log and require a release engineer to review the log before manually clicking the promote button in Cloud Deploy.

  • Configure Cloud Monitoring to alert on high false-positive rates after the new version is fully deployed; if an alert fires, roll back manually and open a defect record.

  • Schedule a nightly Dataflow job that samples production predictions, exports them to Cloud Storage, and emails a QA engineer who manually checks whether precision remains above 99 %.

GCP Professional Cloud Architect
Ensuring solution and operations excellence
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