How to Describe Security Responsibilities for a Managed AI Model on GCP?
Your organization has an operational image classification model running on a managed AI service on Google Cloud. You are in a configuration review with stakeholders and must describe the security responsibilities for the image classification model. What should you do?
Community Votes
100% of anonymous learners picked answer C. Votes are pick records left by other test-takers — they are not the verified answer.
Community Insight
The question tests your understanding of the shared responsibility model, commonly trapping candidates who incorrectly assume full platform delegation or attempt manual infrastructure hardening for fully managed services.
This page clarifies how to articulate cloud security boundaries for managed AI services like Vertex AI. It establishes that reviewing IAM configurations, data handling procedures, and logging aligns with Google’s shared responsibility framework.
Option A is frequently selected because candidates mistakenly believe Platform-as-a-Service completely offloads all security tasks to the provider, ignoring customer-managed data and access controls.
Community Discussion (3 comments)
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Expert Analysis
Why the Answer Is Correct
Selecting option C correctly applies Google Cloud’s shared responsibility model to a managed AI workload. As a customer, you retain ownership of identity management, data classification, and access policies, while Google secures the underlying infrastructure and ML runtime. Focusing the review on IAM permissions, secure data pipelines, and Cloud Audit Logs directly addresses these customer-managed security domains without overstepping into provider responsibilities.Why the Other Options Are Wrong
Option A incorrectly claims that PaaS transfers all security concerns to Google, which violates fundamental cloud security principles regarding data and access control. Option B narrowly focuses only on application code security and team IAM, missing the broader architectural review required for production AI workloads. Option D suggests deploying custom network firewalls and deep intrusion prevention, which contradicts the managed nature of Vertex AI where infrastructure networking is abstracted and controlled by Google.Community Comment Notes
Multiple learners confirmed that option C aligns with official guidance, noting that shared responsibility remains the foundational concept for this certification. One contributor shared the official Vertex AI documentation link to verify how Google delineates platform versus customer duties. Another simply validated that focusing on IAM and logging matches the exam’s emphasis on governance over infrastructure management.Official Reference
Exam Strategy
When reviewing managed services on exams, immediately eliminate options that suggest managing underlying infrastructure or claim total provider liability. Always anchor your response to the shared responsibility model, prioritizing identity, data protection, and observability controls that remain under customer ownership.
Frequently Asked Questions
Why isn't option A correct for managed AI services?
Platform-as-a-Service does not transfer data or access security to Google. Customers remain responsible for protecting uploaded images and controlling who can invoke the model.
Can I deploy custom firewalls around Vertex AI?
No, managed AI services abstract the underlying network layer. Network security relies on VPC Service Controls and IAM rather than custom perimeter appliances.