GCP Professional Cloud Security Engineer Practice Question
Your organization is hardening access to Vertex AI.
The data-science team ([email protected]) must be able to open managed notebooks, launch custom training jobs, and register the resulting Model artifacts. They must not be able to deploy or delete models, update Endpoints, or change IAM policies.
The MLOps team ([email protected]) is responsible for production serving. They need to deploy models to Endpoints and manage traffic splits, but they must not create or modify Datasets. Which assignment of predefined IAM roles best enforces the required least-privilege separation?
Grant both groups the role roles/aiplatform.admin and rely on Cloud Audit Logs for accountability.
The Vertex AI User role lets a principal create and run training pipelines and register Model resources, but it does not grant permissions to deploy models, update Endpoints, or set IAM policies. This satisfies the data-science requirements. The Vertex AI Deployment Resource Admin role is limited to managing online serving resources (Endpoints and deployed models) and does not allow changes to Datasets or training resources, meeting the MLOps needs. Granting broader roles such as Editor or Vertex AI Admin would violate the least-privilege objective, while Viewer would prevent the data-science team from running experiments.
Ask Bash
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What is the purpose of the roles/aiplatform.user IAM role?
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What is the difference between roles/aiplatform.user and roles/aiplatform.deploymentResourceAdmin?
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Why are predefined IAM roles preferred for Vertex AI access control?
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What is the Vertex AI User role?
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What is the Vertex AI Deployment Resource Admin role?
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Why is the least-privilege principle important in IAM roles?
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GCP Professional Cloud Security Engineer
Ensuring data protection
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