PMLE Collaborating to manage data and models Practice Question
A large organization uses a multi-project setup with a central data lake. Different teams manage their own models. To enable cross-team sharing of features, they want to use Vertex AI Feature Store. What is the best practice to manage access?
⚠ Common exam trap
Google Cloud often tests the misconception that separate Feature Stores per team are needed for isolation, but the correct approach is to use a single Feature Store with fine-grained IAM to enable sharing while maintaining security.
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 a single Feature Store in a central project and grant fine-grained IAM roles
Creating a single Feature Store in a central project with fine-grained IAM roles is the best practice because it centralizes feature management while allowing cross-team access control at the feature group or feature level. Vertex AI Feature Store supports IAM roles like `aiplatform.featureStoreAdmin` and `aiplatform.featureStoreDataViewer` to grant granular permissions, enabling teams to share features without duplicating data or exposing sensitive information. This approach avoids data silos and ensures consistent governance across the organization.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Create a single Feature Store in a central project and grant fine-grained IAM roles
Why this is correct
A single central Feature Store lets teams share features across projects, while fine-grained IAM roles restrict each team to only the features and operations it needs. This satisfies the cross-team sharing requirement without granting broad project-level access, which would violate least privilege.
- ✗
Export features to Cloud Storage
Why it's wrong here
Exporting features to Cloud Storage produces static copies that lose Vertex AI Feature Store's online serving, point-in-time correctness and IAM-level feature governance, so cross-team sharing becomes stale file distribution. It is tempting for batch training snapshots, which is a valid scenario, but not for governed online feature sharing.
- ✗
Create separate Feature Stores per team project
Why it's wrong here
Separate Feature Stores per team project fragment the shared feature registry, so cross-team feature discovery and access require duplicated resources and per-store IAM grants rather than one governed store. It is tempting because project isolation suits teams with fully independent features, but here sharing is the stated goal.
- ✗
Use BigQuery authorized views
Why it's wrong here
BigQuery authorised views grant row- or column-level access to query results, but they do not manage Feature Store entity types, feature views or online serving permissions. Feature Store access is controlled through IAM roles on the feature registry and serving resources.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.