PMLE Collaborating to manage data and models Practice Question
You are a machine learning engineer working on a team that uses Vertex AI Feature Store. A colleague has created a new feature and wants to make it available to other teams for training and serving. You need to ensure that the feature can be discovered and reused across projects. What should you do?
⚠ Common exam trap
The trap here is believing that Vertex AI Feature Store has a native public sharing or cross-project visibility toggle, when in fact you need an external catalog like Dataplex.
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
✓
Create the feature in Vertex AI Feature Store and use Dataplex to catalog it, making it searchable and accessible to other teams.
Using Dataplex to catalog Vertex AI Feature Store features enables centralized discovery and metadata management. Other teams can search the catalog, find the feature, and request access, which fosters reuse and collaboration. This approach integrates with existing governance and access controls, unlike ad-hoc sharing methods.
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 Feature Store's built-in feature sharing capability by setting the feature's visibility to 'Public' within the organization.
Why it's wrong here
Vertex AI Feature Store does not have a 'Public' visibility setting for features. Access is controlled via IAM, and there is no built-in cross-project sharing toggle. This option describes a non-existent feature and would mislead the team.
- ✓
Create the feature in Vertex AI Feature Store and use Dataplex to catalog it, making it searchable and accessible to other teams.
Why this is correct
Dataplex provides a centralized data catalog that can include Vertex AI Feature Store features. By cataloging the feature, other teams can discover it through search, understand its metadata, and request access. This promotes reuse and collaboration across projects while maintaining proper governance.
- ✗
Create the feature in a Vertex AI Feature Store instance and share the instance's project ID with other teams.
Why it's wrong here
Sharing the project ID alone does not provide discoverability or access control. Other teams would need IAM permissions and knowledge of the feature's exact location. This approach lacks a centralized catalog for discovery and does not promote reuse across projects.
- ✗
Register the feature in Vertex AI Feature Store and assign it a label that other teams can search for in the Vertex AI console.
Why it's wrong here
Labels can help with filtering but are not a robust discovery mechanism. Other teams would need to know the label and have access to the same Feature Store instance. This does not provide a cross-project catalog and may not scale for organization-wide reuse.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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