Vertex AI Model Deployment IAM Permissions
A team uses Vertex AI Pipelines. They need to ensure that only certain team members can deploy models to production. What is the best approach?
⚠ Common exam trap
A common mix-up: candidates confuse artifact storage permissions (bucket-level IAM) with deployment permissions (model registry IAM), leading them to choose Option B, even though bucket permissions do not control the Vertex AI deployment API call.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use IAM roles with custom permissions on the Vertex AI Model Registry
Vertex AI Model Registry supports IAM roles with custom permissions, allowing fine-grained access control over who can promote or deploy models to production. By assigning specific roles (e.g., `roles/aiplatform.modelDeployer`) to only authorized team members, you can restrict deployment actions while still permitting others to view or register models. This approach directly addresses the need to control production deployments without affecting other pipeline stages.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Vertex AI Experiments to track models
Why it's wrong here
Experiments track experiments, not deployment permissions.
- ✗
Store model artifacts in a bucket with bucket-level permissions
Why it's wrong here
Bucket permissions control storage access, not the deployment action in Vertex AI.
- ✓
Use IAM roles with custom permissions on the Vertex AI Model Registry
Why this is correct
Model Registry integrates with IAM to grant specific deployment permissions.
- ✗
Create separate projects for dev and prod
Why it's wrong here
This provides isolation but does not control which individuals can deploy within a project.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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