PMLE Practice Question: Collaborating Within and Across Teams to Manage Data and Models
A team is building ML pipelines with Vertex AI. They want to reuse standard pipeline components across teams and enforce governance. What approach should they take?
⚠ Common exam trap
PMLE often tests whether candidates choose ad-hoc or manual approaches (shared buckets, independent pipelines) over the standardized, governed Vertex AI Pipelines + component registry pattern, so picking 'shared Cloud Storage bucket' is the common trap.
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 Pipelines with pre-built and custom components organized in a component registry.
Vertex AI Pipelines lets teams define ML workflows as DAGs of containerized components, and organizing those components in a component registry (e.g., Vertex AI's component registry or Artifact Registry) enables reuse across teams. This approach enforces governance through versioning, access control, and standardized interfaces, so teams share vetted components rather than duplicating pipeline logic.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use Vertex AI Pipelines with pre-built and custom components organized in a component registry.
Why this is correct
Vertex AI Pipelines executes containerised components, and storing pre-built and custom components in a component registry lets teams share and version them centrally, enforcing governance and reuse across teams. Components are defined by YAML specs, so standard interfaces are preserved.
- ✗
Store pipeline definitions in a shared Cloud Storage bucket and copy them manually.
Why it's wrong here
A Cloud Storage bucket stores files but provides no component versioning, lineage, or access governance, and manual copying introduces drift. It is tempting as a cheap shared repository for artefacts when teams only need loose file exchange, not enforced pipeline component reuse.
- ✗
Use Cloud Composer to orchestrate ad-hoc scripts.
Why it's wrong here
Cloud Composer orchestrates workflows via Airflow DAGs, but it cannot register versioned components in the Vertex AI Artifact Registry, so cross-team reuse and governance are unenforceable. It is tempting because Composer genuinely suits orchestrating ad-hoc scripts and multi-cloud dependencies outside Vertex AI pipelines.
- ✗
Have each team build their own pipelines independently.
Why it's wrong here
Independent pipelines duplicate component code per team, so no shared registry enforces versioning or approval, defeating reuse and governance. This is tempting when teams need autonomy and rapid iteration on unrelated models, where isolation outweighs standardisation.
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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.