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.
The correct answer is that the IAM policy grants developer@example.com the ability to use Vertex AI models for prediction and view metadata. This is because the assigned role, roles/aiplatform.user, specifically includes permissions like aiplatform.predictions.predict and aiplatform.models.get, which allow invoking models for inference and reading model details, but explicitly excludes write or delete actions such as aiplatform.models.deploy or aiplatform.models.delete. On the Google Cloud Generative AI Leader exam, this question tests your understanding of the principle of least privilege and the granularity of Vertex AI IAM roles and permissions, often appearing as a scenario where a candidate must distinguish between the user, developer, and admin roles. A common trap is confusing the user role with the developer role, which adds deployment capabilities. Memory tip: think of the “user” role as a “consumer” who can only consume predictions and view metadata, not create or manage resources.
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.
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.
✓
Ability to use Vertex AI models for prediction and view metadata.
Why this is correct
roles/aiplatform.user grants permissions to predict, explain, and view resources.
Related concept
Read the scenario before looking for a memorised answer.
✗
No effective permissions because the role is incorrect.
Why it's wrong here
The role is valid and grants specific permissions.
✗
Ability to deploy and manage models.
Why it's wrong here
Deploying models requires roles/aiplatform.deployer or higher.
✗
Full control over all Vertex AI resources.
Why it's wrong here
That requires roles/aiplatform.admin, not user.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Under the hood, the 'Vertex AI User' role is a predefined IAM role that bundles specific permissions such as `aiplatform.predict`, `aiplatform.explanations`, and `aiplatform.models.list`, but explicitly excludes write or management permissions like `aiplatform.models.create` or `aiplatform.endpoints.update`. In a real-world scenario, this role is ideal for data scientists or developers who need to invoke predictions from deployed models without risking accidental changes to the model registry or endpoints.
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: 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.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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