- A
Copy the model artifact from one project's Cloud Storage to another using gsutil.
Why wrong: Manual copy is error-prone and loses model registry information.
- B
Export the model as a SavedModel, store in a shared Cloud Storage bucket, and import into the second project.
Why wrong: This works but does not preserve versioning and lineage.
- C
Package the model in a Docker container and push to a cross-project Container Registry.
Why wrong: Container Registry is deprecated and not designed for model sharing.
- D
Use Cloud Marketplace to publish the model.
Why wrong: Marketplace is for public listing, not internal sharing.
- E
Use Vertex AI Model Registry with cross-project IAM permissions to allow the second project to access the model.
Model Registry maintains version history and metadata while enabling cross-project sharing.
Quick Answer
The answer is to use Vertex AI Model Registry with cross-project IAM permissions, as this allows the second project to directly deploy the model without copying artifacts. This is correct because the Model Registry acts as a centralized, versioned repository that supports granular IAM roles, enabling you to grant a service account or user in another project the `aiplatform.modelDeployments.create` permission on the specific model resource. This maintains a single source of truth, avoids data duplication, and preserves built-in lineage tracking. On the Google Professional Machine Learning Engineer exam, this tests your understanding of secure, scalable model governance versus less optimal methods like exporting model artifacts to Cloud Storage or copying the model across projects. A common trap is assuming you must export and re-import the model, which breaks version history and introduces drift. Remember the memory tip: “Registry, not Copy—IAM keeps the lineage.”
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 team wants to share a trained model with another team who will deploy it to a different Google Cloud project. Which is the recommended way to transfer the model?
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 cross-project IAM permissions to allow the second project to access the model.
Option E is correct because Vertex AI Model Registry supports cross-project access via IAM permissions, allowing the second project to directly deploy the model without copying artifacts. This approach maintains a single source of truth, avoids data duplication, and leverages Vertex AI's built-in versioning and lineage tracking. It is the recommended pattern for sharing models across projects in Google Cloud.
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.
- ✗
Copy the model artifact from one project's Cloud Storage to another using gsutil.
Why it's wrong here
Manual copy is error-prone and loses model registry information.
- ✗
Export the model as a SavedModel, store in a shared Cloud Storage bucket, and import into the second project.
Why it's wrong here
This works but does not preserve versioning and lineage.
- ✗
Package the model in a Docker container and push to a cross-project Container Registry.
Why it's wrong here
Container Registry is deprecated and not designed for model sharing.
- ✗
Use Cloud Marketplace to publish the model.
Why it's wrong here
Marketplace is for public listing, not internal sharing.
- ✓
Use Vertex AI Model Registry with cross-project IAM permissions to allow the second project to access the model.
Why this is correct
Model Registry maintains version history and metadata while enabling cross-project sharing.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that copying artifacts (gsutil) or using shared storage is the simplest approach, but the exam expects candidates to recognize that Vertex AI Model Registry with cross-project IAM is the recommended, managed solution for model sharing across projects.
Detailed technical explanation
How to think about this question
Vertex AI Model Registry uses IAM roles like 'aiplatform.models.get' and 'aiplatform.models.use' to grant cross-project access, enabling the second project to deploy the model via the registry without copying the underlying Cloud Storage blobs. Under the hood, the registry stores a reference to the model artifact location, and IAM policies are evaluated at runtime to authorize access. In a real-world scenario, this allows a central ML team to maintain a single model repository while multiple deployment teams in different projects can deploy the latest approved version with full audit trails.
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 within and across teams to manage data and models — study guide chapter
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Collaborating within and across teams to manage data and models practice questions
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PMLE practice test guide
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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 cross-project IAM permissions to allow the second project to access the model. — Option E is correct because Vertex AI Model Registry supports cross-project access via IAM permissions, allowing the second project to directly deploy the model without copying artifacts. This approach maintains a single source of truth, avoids data duplication, and leverages Vertex AI's built-in versioning and lineage tracking. It is the recommended pattern for sharing models across projects in Google Cloud.
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 →
Last reviewed: Jun 30, 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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