PMLE Practice Question: Collaborating Within and Across Teams to Manage Data and Models
A data science team wants to share engineered features across multiple projects while ensuring low-latency serving for online predictions. Which Google Cloud service should they use to store and serve these features?
⚠ Common exam trap
PMLE often tests the distinction between storing models (Model Registry) and storing features (Feature Store) — candidates conflate the two because both are 'Vertex AI' artifacts.
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 store, share, and serve machine learning features with low-latency online serving and consistent offline serving for training. It lets a data science team centralize engineered features so multiple projects reuse them, while providing an online serving endpoint that returns feature values in milliseconds for real-time predictions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Vertex AI Model Registry
Why it's wrong here
Vertex AI Model Registry catalogues and versions trained models; it stores no feature values and offers no online feature-serving endpoint. It is the right choice for tracking model lineage and deployment, not for sharing engineered features across projects with low-latency retrieval.
- ✗
Cloud Storage
Why it's wrong here
Cloud Storage is blob storage with no feature-serving API, so online predictions cannot retrieve feature values at low latency. It suits storing training datasets and model artefacts, not serving engineered features to a model endpoint that requires millisecond lookups.
- ✗
BigQuery
Why it's wrong here
BigQuery is an analytical warehouse, not a low-latency online feature store; its query latency cannot meet millisecond serving. It is tempting because it stores and SQL-queries engineered features for training, and would be the right choice for batch feature exploration or offline model training rather than real-time prediction serving.
- ✓
Vertex AI Feature Store
Why this is correct
Vertex AI Feature Store provides a centralised repository for engineered features with low-latency online serving, letting multiple projects reuse the same feature definitions. This satisfies both the sharing requirement and the online prediction latency constraint.
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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.