The answer is that the model’s resource-level IAM policy only grants the `roles/aiplatform.user` role, which lacks the `aiplatform.models.deploy` permission required for deployment. While the `roles/aiplatform.user` role at the project level allows using Vertex AI resources like viewing models or endpoints, it does not authorize the act of deploying a model to an endpoint—that action is governed by the model’s own resource-level IAM policy. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding that IAM permissions in Vertex AI are hierarchical: project-level roles grant broad access, but resource-level policies can override or restrict specific actions like deployment. A common trap is assuming the project-level `aiplatform.user` role is sufficient for all operations, when in fact deployment requires `roles/aiplatform.admin` or a custom role with the deploy permission. Memory tip: “User can view, Admin can deploy”—project-level User lets you see resources, but only resource-level Admin or a custom role with deploy can push a model live.
PMLE Collaborating to manage data and models Practice Question
This PMLE practice question tests your understanding of collaborating to manage data and models. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
{
"bindings": [
{
"role": "roles/aiplatform.user",
"members": [
"user:alice@example.com",
"serviceAccount:sa-training@my-project.iam.gserviceaccount.com"
]
}
]
}
This IAM policy is attached to a Vertex AI model resource. Alice can view the model but cannot deploy it to an endpoint. The service account can use the model for training.
Refer to the exhibit. An ML engineer in the team needs to deploy the model to an endpoint. The engineer is assigned the 'roles/aiplatform.user' role at the project level but still cannot deploy. What is the most likely reason?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Refer to the exhibit.
{
"bindings": [
{
"role": "roles/aiplatform.user",
"members": [
"user:alice@example.com",
"serviceAccount:sa-training@my-project.iam.gserviceaccount.com"
]
}
]
}
This IAM policy is attached to a Vertex AI model resource. Alice can view the model but cannot deploy it to an endpoint. The service account can use the model for training.
A
The service account 'sa-training' is using all the model's quota.
Why wrong: Quota is not the issue; permissions are.
B
Alice does not have any IAM role on the project.
Why wrong: She has roles/aiplatform.user at project level, but the resource policy may be more restrictive.
C
Alice needs to be granted the 'roles/aiplatform.admin' role at the project level.
Why wrong: Even with project-level admin, the resource policy still denies deploy if not granted on the resource.
D
The model's resource-level IAM policy only grants the 'roles/aiplatform.user' role, which does not include deploy permission.
The resource policy overrides project-level roles and lacks deploy.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The model's resource-level IAM policy only grants the 'roles/aiplatform.user' role, which does not include deploy permission.
The 'roles/aiplatform.user' role at the project level grants permissions to use AI Platform resources, but it does not include the 'aiplatform.models.deploy' permission required to deploy a model to an endpoint. Model deployment is controlled by resource-level IAM policies, and if the model's resource-level policy only grants 'roles/aiplatform.user', the deploy action is denied. The correct role for deployment is 'roles/aiplatform.admin' or a custom role with the deploy permission.
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.
✗
The service account 'sa-training' is using all the model's quota.
Why it's wrong here
Quota is not the issue; permissions are.
✗
Alice does not have any IAM role on the project.
Why it's wrong here
She has roles/aiplatform.user at project level, but the resource policy may be more restrictive.
✗
Alice needs to be granted the 'roles/aiplatform.admin' role at the project level.
Why it's wrong here
Even with project-level admin, the resource policy still denies deploy if not granted on the resource.
✓
The model's resource-level IAM policy only grants the 'roles/aiplatform.user' role, which does not include deploy permission.
Why this is correct
The resource policy overrides project-level roles and lacks deploy.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between project-level and resource-level IAM policies, where candidates assume that a project-level role automatically grants all permissions on child resources, ignoring that resource-level policies can be more restrictive.
Detailed technical explanation
How to think about this question
In Google Cloud AI Platform, IAM policies are hierarchical: project-level roles grant permissions to all resources in the project, but resource-level policies (e.g., on a specific model) can override or restrict them. The 'aiplatform.models.deploy' permission is required for deployment, and it is included in 'roles/aiplatform.admin' but not in 'roles/aiplatform.user'. When a resource-level policy denies a permission, even a project-level role with that permission is ineffective, leading to a PERMISSION_DENIED error.
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.
Collaborating to manage data and models — This question tests Collaborating to manage data and models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The model's resource-level IAM policy only grants the 'roles/aiplatform.user' role, which does not include deploy permission. — The 'roles/aiplatform.user' role at the project level grants permissions to use AI Platform resources, but it does not include the 'aiplatform.models.deploy' permission required to deploy a model to an endpoint. Model deployment is controlled by resource-level IAM policies, and if the model's resource-level policy only grants 'roles/aiplatform.user', the deploy action is denied. The correct role for deployment is 'roles/aiplatform.admin' or a custom role with the deploy permission.
What should I do if I get this PMLE 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: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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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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. 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?
medium
A.Use Vertex AI Experiments to track models
B.Store model artifacts in a bucket with bucket-level permissions
✓ C.Use IAM roles with custom permissions on the Vertex AI Model Registry
D.Create separate projects for dev and prod
Why C: Option C is correct because 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.
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Question Discussion
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