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Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions

Exhibit

{
  "bindings": [
    {
      "role": "roles/aiplatform.user",
      "members": ["user:developer@example.com"]
    }
  ],
  "etag": "BwWl3Z8="
}

Refer to the exhibit. What access does the IAM policy grant to developer@example.com?

⚠ Common exam trap

Google often tests the distinction between predefined IAM roles (e.g., Vertex AI User vs. Vertex AI Admin) and the specific permissions each grants, trapping candidates who assume any role with 'Vertex AI' in the name provides broad access.

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

✓

Ability to use Vertex AI models for prediction and view metadata.

The IAM policy grants the 'Vertex AI User' role to developer@example.com, which includes permissions for using models for prediction (e.g., `aiplatform.predict`) and viewing metadata (e.g., `aiplatform.models.list`). This role does not include permissions for deploying or managing models, nor full control over all Vertex AI resources, making option A correct.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✓
Ability to use Vertex AI models for prediction and view metadata.
Why this is correct
The IAM policy binds developer@example.com to a role granting Vertex AI prediction permissions plus metadata read access. This satisfies the exhibit's scope: the member can invoke models for prediction and view associated metadata, but nothing broader.
✗
No effective permissions because the role is incorrect.
Why it's wrong here
The binding is syntactically valid, so permissions are not void; the role's permission set determines the grant. This option would be right only if the role name were misspelled or nonexistent, which the exhibit does not show.
✗
Ability to deploy and manage models.
Why it's wrong here
Deploying and managing models requires a role granting model deployment permissions, which the exhibit's binding does not confer on developer@example.com. This would be correct if the policy bound a Vertex AI model-management role to that principal.
✗
Full control over all Vertex AI resources.
Why it's wrong here
An IAM policy granting full control over all Vertex AI resources would require roles/aiplatform.admin bound at project level; the exhibit scopes permissions to a specific resource, so this overstates the grant. It tempts because admin roles are the standard way to grant broad Vertex AI access when a developer genuinely needs unrestricted control across the project.

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Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions

Exhibit

Why each option matters

Option-by-option breakdown

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