The answer is to share the model ID and grant colleagues the 'vertex.ai.models.get' permission. This is correct because Vertex AI Model resources are scoped to a single Google Cloud project, and IAM permissions allow you to share a vertex ai model without exposing project infrastructure or credentials—colleagues access the model via its fully qualified resource name like 'projects/{project}/locations/{region}/models/{model}'. On the Google Professional Machine Learning Engineer exam, this tests your understanding of IAM-based resource sharing versus copying or exporting models, a common trap being to assume you must move the model to another project. The key insight is that Vertex AI uses IAM for fine-grained access control, so granting the 'roles/aiplatform.user' role or the specific 'vertex.ai.models.get' permission is the recommended way to share a model without exposing the underlying project. Memory tip: think "ID + IAM, not copy and scram"—share the identifier, not the infrastructure.
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 data scientist wants to share a trained model with colleagues for evaluation. The model is stored as a Vertex AI Model resource. What is the recommended way to share the model without exposing the underlying project?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Share the model ID and grant colleagues the 'vertex.ai.models.get' permission.
Option A is correct because Vertex AI Model resources are managed within a single Google Cloud project, and the recommended way to share a model without exposing the underlying project is to grant the IAM role 'roles/aiplatform.user' or the specific permission 'vertex.ai.models.get' to the colleagues' Google accounts. This allows them to access the model via the model ID (a fully qualified resource name like 'projects/{project}/locations/{region}/models/{model}') without needing to copy or expose the project's infrastructure or credentials.
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.
✓
Share the model ID and grant colleagues the 'vertex.ai.models.get' permission.
Why this is correct
This provides secure, traceable access without exposing the project.
Related concept
Read the scenario before looking for a memorised answer.
✗
Create a new project and copy the model.
Why it's wrong here
Duplicate projects increase management overhead and cost.
✗
Upload the model to a public Cloud Storage bucket.
Why it's wrong here
Public buckets expose the model to anyone, violating security.
✗
Export the model artifact and email it.
Why it's wrong here
Emailing artifacts is insecure and does not support versioning.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that sharing a model requires copying or exporting the artifact, when in fact IAM-based access control on the managed resource is the secure and recommended approach.
Detailed technical explanation
How to think about this question
Under the hood, Vertex AI Model resources are stored as metadata in the Cloud AI Platform API, with the actual model artifacts (e.g., SavedModel, pickle files) stored in a Cloud Storage bucket within the same project. Granting 'vertex.ai.models.get' permission leverages Google Cloud IAM's resource-level access control, allowing fine-grained access without sharing the project ID or bucket paths. In a real-world scenario, a data scientist might share a model ID like 'projects/my-project/locations/us-central1/models/12345' with a colleague who has the necessary IAM role, enabling them to deploy the model to an endpoint or evaluate it via the Vertex AI SDK without ever seeing the underlying project's billing or other resources.
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.
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: Share the model ID and grant colleagues the 'vertex.ai.models.get' permission. — Option A is correct because Vertex AI Model resources are managed within a single Google Cloud project, and the recommended way to share a model without exposing the underlying project is to grant the IAM role 'roles/aiplatform.user' or the specific permission 'vertex.ai.models.get' to the colleagues' Google accounts. This allows them to access the model via the model ID (a fully qualified resource name like 'projects/{project}/locations/{region}/models/{model}') without needing to copy or expose the project's infrastructure or credentials.
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
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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 data scientist wants to share a trained model with the team for review before deployment. The model is stored in Vertex AI Model Registry. What is the recommended way to grant the team read access to the model?
easy
A.Grant the IAM role 'roles/aiplatform.admin' to the team members.
B.Export the model as a local file and share it via a shared drive.
✓ C.Grant the IAM role 'roles/aiplatform.viewer' to the team members on the project.
D.Add the team members to the Cloud Storage bucket ACL with 'READER' access.
Why C: Option A is correct because Vertex AI Model Registry uses Cloud IAM, and granting the 'roles/aiplatform.viewer' role provides read access to all model versions. Option B is wrong because too broad. Option C is wrong because Cloud Storage IAM is separate and not sufficient for Vertex AI models. Option D is wrong because the bucket ACL does not apply to Vertex AI.
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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.
Question Discussion
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