PMLE Practice Question: Collaborating Within and Across Teams to Manage Data and Models
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?
⚠ Common exam trap
PMLE often tests whether candidates confuse metadata (Data Catalog), model (Model Registry), and feature (Feature Store) services — the trap is picking a storage or catalog service when the requirement is centralized feature serving with consistency.
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
✓
Vertex AI Feature Store
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.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cloud Data Catalog
Why it's wrong here
Cloud Data Catalog is a metadata discovery and governance service; it indexes and searches data assets but does not compute, store or serve feature values for online inference. It is tempting because it centralises data documentation, and would be correct when the requirement is cataloguing and discovering datasets across an organisation.
- ✗
Vertex AI Model Registry
Why it's wrong here
Vertex AI Model Registry tracks model versions, artefacts and deployment lineage; it stores no feature values and cannot serve them to training or online inference. It is tempting because it centralises ML artefacts, and would be correct when the requirement is managing model versioning and promotion to endpoints.
- ✓
Vertex AI Feature Store
Why this is correct
Vertex AI Feature Store provides a central registry where feature definitions are authored once and served consistently to both training and online inference, removing manual Cloud Storage coordination errors. It also supplies point-in-time correctness, which prevents training-serving skew.
- ✗
Cloud Storage with versioning
Why it's wrong here
Cloud Storage with versioning only retains object revisions; it cannot serve features at low-latency for online inference or guarantee point-in-time consistency between training and serving. It is tempting because versioning does preserve historical feature snapshots, which suits batch archival or rollback scenarios, but centralised feature management requires Vertex AI Feature Store.
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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.