- A
Use Vertex AI Workbench with user-managed notebooks.
Why wrong: Notebooks are for development, not deployment enforcement.
- B
Use custom roles with permissions to deploy models, and use Cloud Audit Logs to monitor deployments.
Why wrong: Monitoring does not enforce validation before deployment.
- C
Use Binary Authorization to ensure models are signed.
Why wrong: Binary Authorization is for container images, not model validation.
- D
Use organization policies to restrict deployment to specific locations.
Why wrong: Location restriction does not enforce validation.
- E
Use Vertex AI Model Registry with automated deployment via Cloud Build, and restrict those permissions to the ML Engineering team using IAM conditions.
This ensures only approved pipelines trigger deployment and only authorized team can initiate.
Quick Answer
The answer is to use Vertex AI Model Registry with automated deployment via Cloud Build, and restrict those permissions to the ML Engineering team using IAM conditions. This configuration is correct because it enforces that only models which have passed the validation pipeline can be promoted to production, while IAM conditions ensure that deployment permissions are scoped exclusively to the ML Engineering team, preventing unauthorized business units from bypassing governance. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of combining IAM roles for controlling ML model deployments in Vertex AI with service-level controls—a common trap is assuming a simple role like Vertex AI User is sufficient, but that would allow any user to deploy unvalidated models. Remember the key principle: separate the validation pipeline from the deployment trigger using Model Registry, then lock down the deployment action with conditional IAM. A useful memory tip is “Register, Validate, then Gate”—models must be registered in the registry, validated by the pipeline, and only then can the gated IAM condition allow the ML Engineering team to deploy.
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. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 business units using the same Vertex AI environment. They need to enforce that models deployed to production have passed a validation pipeline, and only the ML Engineering team can deploy to production. Which IAM configuration should they use?
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
Use Vertex AI Model Registry with automated deployment via Cloud Build, and restrict those permissions to the ML Engineering team using IAM conditions.
Option E is correct because it combines Vertex AI Model Registry (which enforces that only validated models are promoted to production) with Cloud Build for automated deployment, and uses IAM conditions to restrict deployment permissions exclusively to the ML Engineering team. This ensures that models must pass the validation pipeline before deployment, and only authorized personnel can trigger the deployment process.
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.
- ✗
Use Vertex AI Workbench with user-managed notebooks.
Why it's wrong here
Notebooks are for development, not deployment enforcement.
- ✗
Use custom roles with permissions to deploy models, and use Cloud Audit Logs to monitor deployments.
Why it's wrong here
Monitoring does not enforce validation before deployment.
- ✗
Use Binary Authorization to ensure models are signed.
Why it's wrong here
Binary Authorization is for container images, not model validation.
- ✗
Use organization policies to restrict deployment to specific locations.
Why it's wrong here
Location restriction does not enforce validation.
- ✓
Use Vertex AI Model Registry with automated deployment via Cloud Build, and restrict those permissions to the ML Engineering team using IAM conditions.
Why this is correct
This ensures only approved pipelines trigger deployment and only authorized team can initiate.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse monitoring (Audit Logs) or location restrictions (Organization Policies) with enforcing a validation pipeline and team-specific deployment permissions, missing the need for a model registry and automated deployment with IAM conditions.
Detailed technical explanation
How to think about this question
Vertex AI Model Registry allows you to manage model versions and promote models through stages (e.g., staging to production) only after they pass validation checks, such as evaluation metrics or automated tests. Cloud Build can be configured to trigger deployment only when a model version is approved in the registry, and IAM conditions (e.g., using the `resource.name` attribute) can restrict who can invoke the deployment pipeline. This pattern is common in MLOps where compliance requires that only validated models reach production, and access is tightly controlled to prevent unauthorized deployments.
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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Collaborating within and across teams to manage data and models — study guide chapter
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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: Use Vertex AI Model Registry with automated deployment via Cloud Build, and restrict those permissions to the ML Engineering team using IAM conditions. — Option E is correct because it combines Vertex AI Model Registry (which enforces that only validated models are promoted to production) with Cloud Build for automated deployment, and uses IAM conditions to restrict deployment permissions exclusively to the ML Engineering team. This ensures that models must pass the validation pipeline before deployment, and only authorized personnel can trigger the deployment process.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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Last reviewed: Jun 24, 2026
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