professional-cloud-data-engineer
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A data engineering team is designing an automated CI/CD pipeline in Google Cloud to build and deploy custom containerized data processing applications. The solution must adhere to enterprise security and deployment standards:
Which combination of actions should the team implement?
This architecture establishes a secure, repeatable, and least-privilege CI/CD pipeline for containerized data pipelines by integrating Artifact Registry, Cloud IAM, and dedicated secret management systems such as Secret Manager or Cloud KMS.
latest tags) and ensures full auditability and traceability back to source code commits.roles/artifactregistry.writer) role provides exact permissions to write and upload build artifacts without giving broader project-level administrative rights.cloudbuild.yaml or application images, enabling the application to securely fetch and decrypt secrets at runtime.Decoupling secrets from container images while applying targeted IAM roles ensures adherence to defense-in-depth security principles. Fetching credentials programmatically at startup guarantees that sensitive tokens are kept strictly in memory during execution.
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