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GCP Professional Cloud Security Engineer Practice Question

A retail enterprise is migrating its ML training workloads from self-managed GPU clusters to Vertex AI custom training. Compliance rules state that:

  • Training data and model artifacts must be encrypted with customer-managed keys.
  • No control plane or data plane traffic may traverse the public internet.
  • The ML team must not be responsible for operating-system patching.
    Which design best satisfies these requirements while taking advantage of the PaaS nature of Vertex AI?

  • Create a VPC Service Controls perimeter that includes Vertex AI and Cloud Storage, enable CMEK encryption on all Vertex AI resources, and access the Vertex AI and Cloud Storage APIs through Private Service Connect endpoints.

  • Store training data in a Cloud Storage bucket with uniform bucket-level access, rely on Google default encryption, and route Vertex AI traffic through Cloud NAT to the public endpoints.

  • Run training jobs on Confidential VMs in Compute Engine, manage guest OS patching manually, and use CMEK for the VM persistent disks only.

  • Establish a VPN from on-premises to Google Cloud and restrict Vertex AI API access by source IP firewall rules while keeping Google default encryption at rest and in transit.

GCP Professional Cloud Security Engineer
Ensuring data protection
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