PMLE Practice Question: Collaborating Within and Across Teams to Manage Data and Models
A team wants to share feature definitions across multiple projects in their organization using Vertex AI Feature Store. What is the recommended approach?
⚠ Common exam trap
The trap is thinking feature views or BigQuery exports provide cross-project sharing; the correct pattern is a centralized feature store with IAM grants, which candidates often overlook in favor of data duplication.
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 a centralized feature store in a shared project and grant access to other projects via IAM
Vertex AI Feature Store is project-scoped, so to share features across projects, the recommended pattern is to create a centralized feature store in a shared (host) project and grant IAM roles to users/service accounts in other projects. This avoids duplication and ensures a single source of truth for feature definitions and online serving.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Export features to BigQuery datasets in each project
Why it's wrong here
Exporting to BigQuery moves feature values into tables, severing the online serving path and versioning that Feature Store provides, so definitions are not truly shared. BigQuery export is right when features are consumed only for offline training or batch scoring, not online serving.
- ✗
Use Vertex AI Feature Store's feature view for cross-project access
Why it's wrong here
A feature view is scoped to its own feature store and project; it does not itself grant cross-project access, which requires IAM and a shared store or registry. Feature views are the correct construct when serving a defined feature subset to models within the same project.
- ✗
Create separate feature stores in each project and synchronize them with Dataflow
Why it's wrong here
Dataflow synchronisation copies feature values between stores, so definitions diverge and lineage is lost; Vertex AI Feature Store shares definitions natively through a single store or registry. Replication is tempting when projects must remain isolated for residency or billing, where copies genuinely are required.
- ✓
Use a centralized feature store in a shared project and grant access to other projects via IAM
Why this is correct
A centralized feature store hosted in one shared project lets multiple projects consume identical feature definitions, with IAM policies granting cross-project access. This satisfies the requirement to share definitions organisation-wide while avoiding duplicated, divergent feature stores per project.
Go deeper
Related to this question
About these practice questions
Courseiva writes every PMLE question from scratch — 775 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
Same concept, more angles
1 more way this is tested on PMLE
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. An ML team wants to share feature definitions across multiple projects to reduce training-serving skew and ensure consistency. They currently store features in Cloud Storage and manually coordinate updates, leading to errors. Which Google Cloud service should they use to centrally manage and serve features for both training and online inference?
medium- A.Cloud Data Catalog
- B.Vertex AI Model Registry
- ✓ C.Vertex AI Feature Store
- D.Cloud Storage with versioning
Why C: Vertex AI Feature Store is purpose-built to centrally define, store, and serve ML features for both training (batch) and online inference (low-latency) with a consistent feature definition, which directly eliminates training-serving skew. It provides a single source of truth so teams across projects can share features without manual coordination.
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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.