The correct answer is to use folders in Google Cloud Resource Manager and assign IAM roles at the folder level. This approach is recommended because Vertex AI IAM hierarchical access control using folders allows policies to be inherited by all projects within that folder, enabling you to grant roles like roles/aiplatform.user to the data science team and roles/aiplatform.admin to the MLOps team in a single, centralized step. On the Google Professional Machine Learning Engineer exam, this concept tests your understanding of scalable permission management and the principle of least privilege, often appearing as a trap where candidates mistakenly assign roles at the project or resource level, leading to duplication and maintenance overhead. The key insight is that folder-level IAM eliminates per-project configuration while ensuring consistent access across multiple ML environments. Remember the mnemonic “Folders First” — always look for a folder-level solution when managing cross-team access to Vertex AI resources.
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. 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.
A company has multiple teams that need to access and manage ML models in Vertex AI. Different teams require different permission levels: the data science team should be able to create and update models, while the MLOps team should have full control. What is the recommended approach to manage access?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use folders in Google Cloud Resource Manager and assign IAM roles at the folder level
Option B is correct because Google Cloud Resource Manager folders allow hierarchical IAM policy inheritance, enabling you to assign roles like 'roles/aiplatform.user' (for data science) and 'roles/aiplatform.admin' (for MLOps) at the folder level. This approach scales across multiple projects within the folder, ensuring consistent permissions without per-project duplication. It aligns with the principle of least privilege and centralized access management for Vertex AI resources.
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.
✗
Grant the 'aiplatform.user' role to a Google Group containing all users
Why it's wrong here
Giving all users the same role does not differentiate permissions between teams.
✓
Use folders in Google Cloud Resource Manager and assign IAM roles at the folder level
Why this is correct
Folders allow hierarchical policy management, and IAM roles can be scoped appropriately for each team.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use labels and tags on models to control access
Why it's wrong here
Labels and tags are for metadata and billing, not IAM permissions.
✗
Create a separate Google Cloud project for each team
Why it's wrong here
Projects per team increase overhead and prevent easy sharing of models across teams.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse resource labels/tags (which are for organization and cost allocation) with IAM-based access control, leading them to incorrectly select Option C as a viable permission management method.
Detailed technical explanation
How to think about this question
IAM roles at the folder level are inherited by all projects and resources within that folder, but you can also set organization-level policies to restrict inheritance (e.g., using 'Deny' rules). Vertex AI custom roles can be crafted with specific permissions like 'aiplatform.models.create' and 'aiplatform.models.update' for data science, while MLOps gets 'aiplatform.models.*' plus 'aiplatform.endpoints.*'. In practice, using folders with separate IAM bindings for each team’s Google Group avoids the need to manage permissions per model version or endpoint, which is critical when teams scale to hundreds of models.
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 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: Use folders in Google Cloud Resource Manager and assign IAM roles at the folder level — Option B is correct because Google Cloud Resource Manager folders allow hierarchical IAM policy inheritance, enabling you to assign roles like 'roles/aiplatform.user' (for data science) and 'roles/aiplatform.admin' (for MLOps) at the folder level. This approach scales across multiple projects within the folder, ensuring consistent permissions without per-project duplication. It aligns with the principle of least privilege and centralized access management for Vertex AI resources.
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.
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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