- A
Use Vertex AI Model Registry's cross-project sharing feature with IAM conditions.
Model Registry supports sharing model versions across projects with fine-grained IAM.
- B
Publish the model to Google Cloud Marketplace.
Why wrong: Marketplace is for public sharing, not internal.
- C
Export the model to a Cloud Storage bucket accessible by both projects and import into the second project.
This preserves model artifacts and allows import into Model Registry in the second project.
- D
Use IAM to grant the second project's service account Vertex AI User role on the model resource.
IAM allows cross-project access to the model directly.
- E
Use the gcloud ai models copy command to copy the model across projects.
Why wrong: No such command exists; models are exported/imported.
Quick Answer
The answer is using IAM to grant the second project's service account the Vertex AI User role on the model resource. This is correct because Vertex AI Model Registry supports cross-project sharing at the resource level, allowing you to apply IAM policies directly to a model without duplicating or moving the underlying artifacts. On the Google Professional Machine Learning Engineer exam, this tests your understanding of IAM conditions and resource-level permissions versus project-level access, a common trap where candidates mistakenly think models must be exported or copied between projects. The key insight is that Vertex AI models are not siloed by project—they are accessible via IAM bindings, making this a secure and efficient sharing method. For a memory tip, think "IAM on the model, not the project" to avoid the trap of assuming cross-project access requires moving data.
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.
Which THREE of the following are valid ways to share a Vertex AI model across two different Google Cloud projects?
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's cross-project sharing feature with IAM conditions.
Option A is correct because Vertex AI Model Registry supports cross-project sharing by allowing you to grant IAM roles with conditions on the model resource. This enables a model registered in one project to be accessed by a service account from another project without moving or copying the model artifacts.
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 Model Registry's cross-project sharing feature with IAM conditions.
Why this is correct
Model Registry supports sharing model versions across projects with fine-grained IAM.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Publish the model to Google Cloud Marketplace.
Why it's wrong here
Marketplace is for public sharing, not internal.
- ✓
Export the model to a Cloud Storage bucket accessible by both projects and import into the second project.
Why this is correct
This preserves model artifacts and allows import into Model Registry in the second project.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use IAM to grant the second project's service account Vertex AI User role on the model resource.
Why this is correct
IAM allows cross-project access to the model directly.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use the gcloud ai models copy command to copy the model across projects.
Why it's wrong here
No such command exists; models are exported/imported.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may assume a dedicated copy command exists for moving models across projects, but Vertex AI relies on IAM-based sharing or export/import workflows instead.
Trap categories for this question
Command / output trap
No such command exists; models are exported/imported.
Detailed technical explanation
How to think about this question
Under the hood, Vertex AI Model Registry stores model metadata and artifact references in a regional Cloud Storage bucket. Cross-project sharing via IAM conditions works by granting the `aiplatform.models.get` and `aiplatform.models.predict` permissions on the model resource to a service account from another project, allowing it to deploy or use the model without duplicating the underlying artifacts. This approach is critical in multi-tenant environments where a central ML team trains models and shares them with application teams in separate projects.
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 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's cross-project sharing feature with IAM conditions. — Option A is correct because Vertex AI Model Registry supports cross-project sharing by allowing you to grant IAM roles with conditions on the model resource. This enables a model registered in one project to be accessed by a service account from another project without moving or copying the model artifacts.
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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