GCP Professional Cloud Security Engineer Practice Question
Your organization is migrating from self-managed TensorFlow training on Compute Engine VMs to Vertex AI custom training jobs. Compliance auditors ask which security controls still remain under your team's responsibility after the move, given that Vertex AI is delivered as a fully managed PaaS. Which control is the customer still accountable for?
Maintaining hypervisor isolation between customer workloads on shared host machines
Applying critical OS patches to the worker nodes that run training containers
Configuring IAM permissions that govern who can submit training jobs and access model artifacts
Encrypting training data at rest using Google-managed server-side encryption keys
With Vertex AI, Google patches and manages the underlying hosts, hypervisor, and operating systems, and automatically encrypts data at rest with Google-managed keys. The customer, however, must still define and maintain least-privilege IAM policies that determine who can submit training jobs, read datasets, and access model artifacts. Therefore the responsibility for configuring IAM permissions remains with the customer, while the other listed items are handled by Google.
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What is IAM and how does it work in a cloud environment?
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What does 'least privilege' mean in IAM configurations?
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Why is encryption at rest handled automatically in Vertex AI?
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What is IAM, and why is it vital in Vertex AI?
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How does Google manage encryption for Vertex AI data?
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What aspects of infrastructure management does Google handle in Vertex AI?
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GCP Professional Cloud Security Engineer
Ensuring data protection
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