PMLE Practice Question: Collaborating Within and Across Teams to Manage Data and Models
A junior engineer on your team has a trained scikit-learn model saved as a local joblib file and wants other teams to be able to discover it, view its evaluation metrics, and deploy it to a Vertex AI Endpoint. Which action should they take first?
⚠ Common exam trap
The trap here is treating a shared file location as sufficient for model sharing, when cross-team discovery and deployment require registration in a model catalog with serving metadata.
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
✓
Upload the model artifact to Vertex AI Model Registry with its serving container and metadata.
To let other teams discover, evaluate, and deploy a model, it must be registered in a shared catalog. Vertex AI Model Registry stores the artifact, its serving container, and metadata such as evaluation metrics, and it integrates directly with endpoints for deployment. Git, raw Cloud Storage objects, and manual node manipulation all lack the cataloging, versioning, and deployment integration the scenario requires.
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 Vertex AI Endpoint and manually copy the joblib file onto the underlying prediction nodes.
Why it's wrong here
Vertex AI Endpoints do not expose prediction nodes for manual file placement; models are deployed from Model Registry or an artifact URI through a serving container. Attempting this bypasses versioning and reproducibility entirely, and the deployment would fail because there is no registered model resource. This is not a supported or sensible approach.
- ✓
Upload the model artifact to Vertex AI Model Registry with its serving container and metadata.
Why this is correct
Vertex AI Model Registry is the central catalog where models become discoverable, versioned, and deployable to endpoints. Importing the joblib artifact with the appropriate pre-built scikit-learn serving container and attaching evaluation metrics gives other teams visibility and a one-click path to deployment. This is the correct first step for sharing a model across teams.
- ✗
Copy the joblib file into a shared Cloud Storage bucket and email the object path.
Why it's wrong here
A Cloud Storage object is just a blob; it carries no model metadata, no metrics, and no registration in a catalog, so discovery across teams is poor and deployment still requires manual container configuration. Object paths also change and are easy to lose. This is a transport mechanism, not a model management solution, so it is not the right first action.
- ✗
Commit the joblib file to the team's Git repository and share the repository link.
Why it's wrong here
Git is suited to source code, not large binary model artifacts, and it offers no model versioning semantics, no evaluation metadata, and no deployment path to an endpoint. Other teams could download the file, but they would have no discoverability of metrics or lineage and would have to build serving infrastructure themselves. This does not meet the sharing requirement.
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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.