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PMLE Practice Question: Two teams are collaborating on a project and want…
Two teams are collaborating on a project and want to use a shared Feature Store in Vertex AI. They need to ensure that features are discoverable and that access is controlled. What is the best practice?
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 Feature Store and grant appropriate IAM roles to each team
Vertex AI Feature Store provides a managed service for sharing features with access controls via IAM roles and enables feature discovery through the UI and API. Option A is wrong because CSV files in Cloud Storage lack feature store metadata, versioning, and online serving capabilities. Option B is wrong because building a custom pipeline with Dataflow and storing in Cloud SQL is not a managed feature store solution and does not provide the same discovery or access control features. Option C is wrong because each team maintaining their own BigQuery table does not offer centralized feature discovery or unified access control; a Feature Store centralizes metadata and permissions.
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 CSV files in Cloud Storage and share the bucket
Why it's wrong here
CSV exports in Cloud Storage provide no feature registry, lineage, or point-in-time serving, and bucket IAM governs objects rather than feature-level access. It would suit bulk offline handoffs. A shared Vertex AI Feature Store supplies discoverability and controlled access natively.
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Build a custom feature pipeline using Dataflow and store in Cloud SQL
Why it's wrong here
A custom Dataflow pipeline into Cloud SQL builds bespoke plumbing with no feature registry, versioning, or online serving, and Cloud SQL IAM controls instances rather than features. It would suit bespoke transactional storage. Vertex AI Feature Store delivers discoverability and access control without custom code.
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Each team stores features in their own BigQuery table and shares the table
Why it's wrong here
Separate BigQuery tables give no unified feature catalogue, so teams cannot discover each other's features, and table-level IAM cannot govern individual features. It would work for independent analytics datasets. The requirement is a shared, discoverable, access-controlled feature repository.
- ✓
Use Vertex AI Feature Store and grant appropriate IAM roles to each team
Why this is correct
Vertex AI Feature Store provides a centralised registry where features are published and searchable across projects, satisfying the discoverability requirement. Granting granular IAM roles per team enforces least-privilege access to specific feature groups and resources, directly meeting the controlled-access constraint without duplicating feature data.
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