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GCP Professional Cloud Architect Practice Question

An enterprise data science team must automate a recurring workflow: ingest events with Dataflow, train a GPU-based model, evaluate it, and deploy a new Vertex AI endpoint only when accuracy improves. Regulations require automatic capture of lineage for every dataset, model, and metric, and auditors need a graphical UI to inspect past runs. The team also wants to rerun the workflow with different parameters and does not want to operate its own orchestration control-plane. Which Google Cloud service best satisfies these needs?

  • Use Vertex AI Pipelines (managed Kubeflow Pipelines) to define and run the workflow, relying on its built-in ML Metadata tracking and console visualization.

  • Use Cloud Composer (managed Apache Airflow) to schedule Dataflow, training, and deployment tasks in a DAG.

  • Chain Cloud Build triggers that invoke gcloud commands for Dataflow, custom training, and endpoint deployment.

  • Connect each step with Pub/Sub topics and Eventarc-triggered Cloud Functions to progress through the workflow.

GCP Professional Cloud Architect
Managing and provisioning a solution infrastructure
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