The best practice to restrict generative AI model deployment is to add an IAM condition that limits deployment to authorized users based on attributes like user role, project, or resource tags. This approach is correct because it enforces fine-grained access control without removing existing permissions or adding unnecessary roles, directly addressing the need to restrict deployment while preserving current user access. On the Google Cloud Generative AI Leader exam, this concept tests your understanding of attribute-based access control (ABAC) versus broad IAM policies—a common trap is assuming you must revoke domain-wide permissions or assign a new role, when a conditional constraint is the precise, least-privilege solution. For memory, think of the “Condition, Not Removal” rule: you never delete access; you simply gate it with a condition.
Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
This Generative AI Leader practice question tests your understanding of business strategies for generative ai solutions. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Exhibit
Refer to the exhibit.
```json
{
"bindings": [
{
"role": "roles/aiplatform.user",
"members": ["user:admin@example.com"]
}
]
}
```
A company wants to ensure only authorized users can deploy gen AI models. The current policy allows all users in the domain. What is the best practice to restrict deployment?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Add condition to restrict deployment
Option C is correct because adding a condition to restrict deployment (e.g., using IAM conditions in AWS, conditional access policies in Azure, or attribute-based access control in GCP) allows you to limit model deployment to only authorized users based on attributes like user role, project, or resource tags. This is the best practice because it enforces fine-grained access control without removing existing permissions or adding unnecessary roles, directly addressing the requirement to restrict deployment while maintaining existing user access.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Remove the binding
Why it's wrong here
Removing the binding removes all access, which is too restrictive.
✗
Add more roles
Why it's wrong here
Adding roles does not restrict; it grants additional permissions.
✓
Add condition to restrict deployment
Why this is correct
Conditions in IAM allow policies like requiring a specific IP range or MFA for deployment actions.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use organizational policies
Why it's wrong here
Organizational policies set constraints but are not as granular as IAM conditions for specific actions.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that organizational policies (Option D) are the catch-all for access control, but they are designed for resource-level governance (e.g., disabling service creation), not for user-specific deployment restrictions, which require conditional IAM policies.
Detailed technical explanation
How to think about this question
Under the hood, conditions in IAM policies (e.g., AWS IAM Condition element with `aws:PrincipalTag` or `aws:RequestTag`) evaluate at runtime against the request context, allowing or denying the `sagemaker:CreateModel` or `vertexai:DeployModel` action based on user attributes. A real-world scenario is a multi-tenant environment where data scientists in different projects should only deploy models to their designated endpoints; a condition like `StringEqualsIfExists` on `sagemaker:ResourceTag/project` ensures cross-project deployment is blocked. This approach avoids the pitfalls of over-permissive roles or overly restrictive organizational policies.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What does this Generative AI Leader question test?
Business Strategies for Generative AI Solutions — This question tests Business Strategies for Generative AI Solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Add condition to restrict deployment — Option C is correct because adding a condition to restrict deployment (e.g., using IAM conditions in AWS, conditional access policies in Azure, or attribute-based access control in GCP) allows you to limit model deployment to only authorized users based on attributes like user role, project, or resource tags. This is the best practice because it enforces fine-grained access control without removing existing permissions or adding unnecessary roles, directly addressing the requirement to restrict deployment while maintaining existing user access.
What should I do if I get this Generative AI Leader question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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1 more ways this is tested on Generative AI Leader
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. An organization uses an IAM policy for Vertex AI as shown. A security audit reveals that engineer@example.com deployed a model that inadvertently exposed sensitive data. What is the most likely reason this happened?
medium
A.Audit logging is not enabled for DATA_WRITE events.
B.The admin user did not review the deployment.
✓ C.The engineer had the aiplatform.user role, which includes permissions to deploy models without additional review.
D.The policy does not include a separation of duties between development and production.
Why C: Option C is correct because the `aiplatform.user` role in Vertex AI includes the `aiplatform.model.deploy` permission, which allows any user with that role to deploy models without requiring additional approvals or administrative review. This lack of a secondary authorization step means the engineer could deploy a model that exposed sensitive data, even if the model had not been properly vetted for data leakage.
Last reviewed: Jun 30, 2026
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This Generative AI Leader practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Generative AI Leader exam.
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