- A
Use Vertex AI Experiments to track models
Why wrong: Experiments track experiments, not deployment permissions.
- B
Store model artifacts in a bucket with bucket-level permissions
Why wrong: Bucket permissions control storage access, not the deployment action in Vertex AI.
- C
Use IAM roles with custom permissions on the Vertex AI Model Registry
Model Registry integrates with IAM to grant specific deployment permissions.
- D
Create separate projects for dev and prod
Why wrong: This provides isolation but does not control which individuals can deploy within a project.
PMLE Collaborating to manage data and models Practice Question
This PMLE practice question tests your understanding of collaborating 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 team uses Vertex AI Pipelines. They need to ensure that only certain team members can deploy models to production. What is the best approach?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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 IAM roles with custom permissions on the Vertex AI Model Registry
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.
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 Experiments to track models
Why it's wrong here
Experiments track experiments, not deployment permissions.
- ✗
Store model artifacts in a bucket with bucket-level permissions
Why it's wrong here
Bucket permissions control storage access, not the deployment action in Vertex AI.
- ✓
Use IAM roles with custom permissions on the Vertex AI Model Registry
Why this is correct
Model Registry integrates with IAM to grant specific deployment permissions.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Create separate projects for dev and prod
Why it's wrong here
This provides isolation but does not control which individuals can deploy within a project.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse artifact storage permissions (bucket-level IAM) with deployment permissions (model registry IAM), leading them to choose Option B, even though bucket permissions do not control the Vertex AI deployment API call.
Detailed technical explanation
How to think about this question
Vertex AI Model Registry uses IAM roles like `roles/aiplatform.modelDeployer` and `roles/aiplatform.modelRegistryAdmin` to control actions such as creating, updating, or deploying model versions. Under the hood, the deployment action invokes the `models.deploy` API, which checks the caller's IAM permissions against the model resource; if the user lacks the `aiplatform.models.deploy` permission, the request is denied with a 403 error. In a real-world scenario, a team might grant `roles/aiplatform.modelViewer` to all members for visibility, but restrict `roles/aiplatform.modelDeployer` to a DevOps lead, ensuring only that lead can push a model to a production endpoint.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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 to manage data and models — study guide chapter
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FAQ
Questions learners often ask
What does this PMLE question test?
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: Use IAM roles with custom permissions on the Vertex AI Model Registry — 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.
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: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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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Last reviewed: Jun 24, 2026
This PMLE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PMLE exam.
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