PMLE Practice Question: Collaborating Within and Across Teams to Manage Data and Models
A company uses Vertex AI Model Registry to manage multiple model versions. They want to designate a model version as 'champion' for production deployment and another as 'challenger' for A/B testing. Which feature of the registry should they use?
⚠ Common exam trap
PMLE often tests the confusion between labels (static tags for filtering) and aliases (mutable pointers for deployment), leading candidates to choose labels for champion/challenger designation.
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
✓
Model aliases
Model aliases in Vertex AI Model Registry let you assign a mutable, named reference (e.g., 'champion', 'challenger') to a specific model version, so deployment endpoints can point to the alias rather than a fixed version ID. This enables seamless promotion or rollback by reassigning the alias, and supports A/B testing by directing traffic to different aliased versions. It is the intended feature for designating champion/challenger roles.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Model version labels
Why it's wrong here
Model version labels are arbitrary key-value metadata for filtering and organisation; Vertex AI does not treat a label as a deployment alias. It is tempting because tagging versions champion and challenger looks like designation, but aliases are the registry feature that resolves a named pointer to a specific version.
- ✗
Model lineage
Why it's wrong here
Model lineage records the provenance and transformation history of artefacts, not production role assignment. Champion/challenger designation requires registry aliases or version labels, which map a specific version to a deployment role. Lineage would be chosen when auditing data provenance or reproducing a training pipeline.
- ✓
Model aliases
Why this is correct
Model aliases are mutable, named pointers (for example 'champion', 'challenger') that reference a specific version within a registered model, so traffic can be switched between versions without redeploying or changing version IDs. This directly supports designating production and A/B testing versions.
- ✗
Model evaluation metrics
Why it's wrong here
Model evaluation metrics report performance scores for a version; they assign no deployment role. It is tempting because champion and challenger selection is metric-driven, but the registry feature that designates which version serves production or A/B traffic is an alias, not the metrics themselves.
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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.