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An enterprise DevOps engineering team is designing a Google Cloud Deploy delivery pipeline to manage application rollouts across three targets: dev, staging, and prod.
The deployment architecture must satisfy the following operational requirements:
staging must automatically promote to prod after a mandatory 30-minute stabilization soak period without manual intervention.prod target uses a multi-phase canary deployment strategy; Cloud Deploy must automatically advance through the canary phases upon successful completion of preceding phases.prod fails, the pipeline must attempt one automatic retry, and if that retry fails, automatically execute a rollback to the previous successful release.Which Cloud Deploy configuration should the team implement to satisfy these requirements?
promoteReleaseRule with a 30-minute wait time, an advanceRolloutRule for the canary target, and a repairRolloutRule configured for retries and rollback.Cloud Deploy Automation provides native, declarative policy definitions that govern release progression and error handling across delivery pipelines. It automates operational rollout tasks such as multi-target promotions, canary phase progressions, and failure remediations without requiring custom external orchestrators.
promoteReleaseRule evaluates the state of the upstream staging target and automatically promotes the release to prod once the rollout succeeds and the specified 30-minute wait duration expires.advanceRolloutRule allows targets configured with a canary deployment strategy to automatically advance from one phase to the next upon successful verification of the previous phase.repairRolloutRule defines automated remediation policies, allowing Cloud Deploy to retry a failed rollout a specified number of times and automatically initiate a rollback to the last successful release if retries are exhausted.gcloud deploy apply.Using native Cloud Deploy automation rules (promoteReleaseRule, advanceRolloutRule, and repairRolloutRule) centralizes release management, canary progression, and automated rollback within Google Cloud's managed control plane rather than relying on disparate external tools.
gcloud deploy rollback upon detecting target failure events.Dataflow is a fully managed stream and batch data processing service built on Apache Beam. It is optimized for large-scale data analytics, ETL pipelines, and real-time event stream processing.
Automation resources that execute automated promotions, canary phase transitions, and rollbacks without external stream processing engines.The organization needed to aggregate, transform, and analyze terabytes of operational telemetry and application logs from production clusters to compute long-term reliability metrics and business KPIs.
Operational failure remediation in CI/CD should always leverage the native capabilities of the deployment platform rather than standing up separate data engineering infrastructure.
Skaffold is a client-side command-line tool that orchestrates building, pushing, and deploying containerized applications. Skaffold profiles and lifecycle hooks run locally or inside build worker containers during the rendering and manifest generation stages.
skaffold render) and applying resources (skaffold apply). It does not manage server-side continuous delivery lifecycle events such as inter-target promotion schedules, soak timers, or multi-phase canary rollouts.The requirement were to customize container build parameters, run client-side integration tests, or perform environment-specific Kustomize overlays before generating Kubernetes manifests.
Skaffold works hand-in-hand with Cloud Deploy, where Skaffold provides the declarative render/deploy engine and Cloud Deploy provides release management, automation rules, and target orchestration.
Cloud Tasks is a fully managed asynchronous task execution service used to dispatch and rate-limit HTTP requests or worker executions. Cloud Workflows is a serverless orchestration engine designed to link Google Cloud APIs and microservices together in structured multi-step workflows.
Automation resources. Building an external orchestration layer using Cloud Tasks and Workflows introduces superfluous operational overhead and custom code.This approach would be appropriate if the organization required custom, complex multi-system governance workflows involving external legacy enterprise systems (e.g., ServiceNow change record ticket approvals) prior to advancing deployments.
Using native pipeline constructs whenever available adheres to Google Cloud DevOps best practices by keeping continuous delivery pipelines declarative and maintainable.
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