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Question 60 of 506

Collaborating within and across teams to manage data and models →hardMultiple ChoiceObjective-mapped

Quick Answer

The answer is that the 'roles/aiplatform.user' role lacks the specific permission required to create custom training jobs using pre-built containers. While this role includes 'aiplatform.customJobs.create', it is intentionally scoped to only allow custom jobs that use managed models or notebooks, not those launched with a pre-built container image. For the latter, you need the 'roles/aiplatform.customJobUser' role, which grants the broader 'aiplatform.customJobs.create' permission without that restriction. On the Google Professional Machine Learning Engineer exam, this is a classic trap: candidates often assume the generic 'user' role covers all custom job creation, but the exam tests the granularity of IAM roles for Vertex AI training workflows. A common memory tip is to think of 'user' as limited to Vertex AI’s managed services, while 'customJobUser' is for your own containers—remember "custom needs custom."

PMLE Practice Question: Collaborating within and across teams to manage data and models

This PMLE practice question tests your understanding of collaborating within and across teams 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",
        "group:data-scientists@example.com"
      ]
    },
    {
      "role": "roles/aiplatform.customCodeServiceAgent",
      "members": [
        "serviceAccount:vertex-ai@project.iam.gserviceaccount.com"
      ]
    }
  ],
  "etag": "BwXahRc1X3w="
}
```

A team has set up the IAM policy above on a Vertex AI project. Alice, a data scientist, reports that she cannot create a Vertex AI Training custom job using a pre-built container. Other data scientists in the group 'data-scientists@example.com' have the same issue. What is the most likely cause?

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.

Question 1hardmultiple choice

Full question →

Exhibit

Refer to the exhibit.

```
{
  "bindings": [
    {
      "role": "roles/aiplatform.user",
      "members": [
        "user:alice@example.com",
        "group:data-scientists@example.com"
      ]
    },
    {
      "role": "roles/aiplatform.customCodeServiceAgent",
      "members": [
        "serviceAccount:vertex-ai@project.iam.gserviceaccount.com"
      ]
    }
  ],
  "etag": "BwXahRc1X3w="
}
```

A
The 'roles/aiplatform.user' role does not grant the permission to create custom training jobs.
Creating custom jobs requires 'aiplatform.customJobs.create', which is not in the aiplatform.user role.
B
The Vertex AI Custom Code Service Agent service account is missing the 'roles/aiplatform.user' role.
Why wrong: The service account has a different role and does not affect user permissions.
C
Alice is not included in the 'data-scientists@example.com' group.
Why wrong: The exhibit shows the group is in the policy, and Alice is presumably a member; the issue is with the role, not group membership.
D
The service account 'vertex-ai@project.iam.gserviceaccount.com' does not have permission to access the training data.
Why wrong: The issue is about job creation, not data access.

Full breakdown with real-world context →

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

✓

The 'roles/aiplatform.user' role does not grant the permission to create custom training jobs.

The 'roles/aiplatform.user' role includes the 'aiplatform.customJobs.create' permission, which is required to create custom training jobs. However, the issue is that Alice and her group cannot create a custom job using a pre-built container. The most likely cause is that the 'roles/aiplatform.user' role does not grant the permission to create custom training jobs with pre-built containers; it only allows using managed models or notebooks. To create custom training jobs, the 'roles/aiplatform.customJobUser' role is needed, which includes the necessary permissions for custom job creation.

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 'roles/aiplatform.user' role does not grant the permission to create custom training jobs.
Why this is correct
Creating custom jobs requires 'aiplatform.customJobs.create', which is not in the aiplatform.user role.
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.
✗
The Vertex AI Custom Code Service Agent service account is missing the 'roles/aiplatform.user' role.
Why it's wrong here
The service account has a different role and does not affect user permissions.
✗
Alice is not included in the 'data-scientists@example.com' group.
Why it's wrong here
The exhibit shows the group is in the policy, and Alice is presumably a member; the issue is with the role, not group membership.
✗
The service account 'vertex-ai@project.iam.gserviceaccount.com' does not have permission to access the training data.
Why it's wrong here
The issue is about job creation, not data access.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Google Cloud often tests the distinction between the 'roles/aiplatform.user' role and more specific roles like 'roles/aiplatform.customJobUser', leading candidates to assume the basic user role covers all Vertex AI actions, when in fact it does not include custom job creation.

Trap categories for this question

Command / output trap
The exhibit shows the group is in the policy, and Alice is presumably a member; the issue is with the role, not group membership.

Detailed technical explanation

How to think about this question

In Vertex AI, the 'roles/aiplatform.user' role is a basic role that allows using Vertex AI resources like notebooks and models but does not include permissions for creating custom training jobs. The 'roles/aiplatform.customJobUser' role explicitly grants 'aiplatform.customJobs.create' and related permissions. Under the hood, IAM policies are evaluated based on the permissions attached to the user or group, and the absence of the custom job permission results in a 403 error when attempting to create a custom job, even if the user has other Vertex AI permissions.

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.

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FAQ

Questions learners often ask

What does this PMLE question test?

Collaborating within and across teams to manage data and models — This question tests Collaborating within and across teams 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 'roles/aiplatform.user' role does not grant the permission to create custom training jobs. — The 'roles/aiplatform.user' role includes the 'aiplatform.customJobs.create' permission, which is required to create custom training jobs. However, the issue is that Alice and her group cannot create a custom job using a pre-built container. The most likely cause is that the 'roles/aiplatform.user' role does not grant the permission to create custom training jobs with pre-built containers; it only allows using managed models or notebooks. To create custom training jobs, the 'roles/aiplatform.customJobUser' role is needed, which includes the necessary permissions for custom job creation.

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.

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Last reviewed: Jun 30, 2026

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