hardMultiple Choice
PMLE Practice Question: Two teams independently develop two different…
Two teams independently develop two different versions of a model for the same use case. They both deploy to the same Vertex AI endpoint, causing conflicts. What is the best way to manage multiple model versions and avoid conflicts in a collaborative environment?
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 Model Registry with staging and production channels, and implement CI/CD to control promotions
Vertex AI Model Registry with staging and production channels provides a centralized system to manage model versions, track lineage, and control promotions via CI/CD pipelines. This prevents conflicts by enforcing a structured workflow for version updates. Option A is wrong because separate projects increase management overhead and do not address versioning within the same endpoint. Option B is wrong because custom metadata lacks enforcement of deployment order and can lead to manual errors. Option C is wrong because deploying to separate endpoints does not resolve version conflicts; it merely isolates models, increasing complexity and cost.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Have each team work on a separate Google Cloud project
Why it's wrong here
Separate projects isolate resources but prevent shared model registry, endpoint, and IAM governance across the two teams, so collaborative version management is lost. It is tempting because project boundaries cleanly separate billing and quotas, and would be correct when teams must be fully isolated for compliance reasons.
- ✗
Use custom metadata to tag each version and rely on team coordination
Why it's wrong here
Metadata tags label versions but enforce nothing, so both teams can still deploy to the same endpoint and overwrite traffic splits. It is tempting because tagging is lightweight and requires no infrastructure change; it would be correct when versions are already isolated by endpoint or registry and tags only aid discovery.
- ✗
Deploy each team's model to a separate endpoint
Why it's wrong here
Separate endpoints isolate deployments but fragment version management, leaving no shared registry or traffic-splitting mechanism to compare or promote versions. It is tempting because it removes immediate conflicts, and would be correct when models serve genuinely distinct use cases with independent scaling and access requirements.
- ✓
Use Vertex AI Model Registry with staging and production channels, and implement CI/CD to control promotions
Why this is correct
Vertex AI Model Registry assigns each version a unique resource ID and tracks lineage, so two teams' artefacts never overwrite each other on one endpoint. Staging and production channels, driven by CI/CD promotions, enforce the controlled release the stem's conflict-avoidance constraint requires.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.