hardMultiple Select
PDE Practice Question: Implement a robust MLOps lifecycle on Google Cloud
A company wants to implement a robust MLOps lifecycle on Google Cloud. Which THREE components are essential?
⚠ Common exam trap
Watch out — candidates often confuse optional supporting services (like Pub/Sub for event triggers or Cloud SQL for metadata) with the essential components required for a robust MLOps lifecycle, which are versioning, orchestration, and CI/CD.
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 Model Registry for versioning
Vertex AI Model Registry (A) is essential because it provides centralized versioning, lineage, and lifecycle tracking of trained models, which is a core requirement for a robust MLOps lifecycle. Vertex AI Pipelines (B) is essential for orchestrating the end-to-end ML workflow (data prep, training, evaluation, deployment) as reproducible, automated pipeline steps. Cloud Build (D) is essential for CI/CD, enabling automated building, testing, and deployment of ML artifacts and pipeline components as part of the MLOps process. Pub/Sub (C) can support event-driven retraining but is an optional architectural pattern rather than a core MLOps component, and Cloud SQL (E) is a general relational database not purpose-built for model metadata management, so neither is essential here.
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 for versioning
Why this is correct
Vertex AI Model Registry provides centralised versioning, lineage and stage tracking for trained models. It satisfies the lifecycle constraint by giving every model artifact an immutable, addressable version that pipelines and endpoints can reference, enabling rollback and auditability across retraining cycles.
- ✓
Vertex AI Pipelines for orchestration
Why this is correct
Vertex AI Pipelines orchestrates the MLOps workflow as reproducible, serverless DAGs covering data preparation, training, evaluation and deployment. It satisfies the lifecycle constraint by automating repeatable execution and tracking artefacts, so retraining runs consistently rather than through manual, error-prone steps.
- ✗
Pub/Sub for event-driven retraining
Why it's wrong here
Pub/Sub delivers asynchronous messages, but it cannot itself trigger, schedule or orchestrate a retraining pipeline; that requires Cloud Scheduler, Cloud Functions or Vertex AI Pipelines. It is tempting because Pub/Sub genuinely underpins event-driven architectures, and would be the right choice when decoupling producers from consumers for streaming ingestion or change notifications.
- ✓
Cloud Build for CI/CD
Why this is correct
Cloud Build supplies the CI/CD layer, building container images and running tests whenever code or pipeline definitions change. It satisfies the lifecycle constraint by automating integration and delivery, ensuring validated artefacts reach Vertex AI consistently instead of relying on manual builds.
- ✗
Cloud SQL for model metadata
Why it's wrong here
Cloud SQL stores relational data but lacks the specialised schema and versioning capabilities for ML metadata tracking; MLOps requires a purpose-built metadata store like Vertex ML Metadata to log model parameters, datasets, and experiment lineage. It is tempting because Cloud SQL is a robust managed database for application state, and would be correct for storing structured business data or user profiles in a traditional web application.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PDE 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 PDE exam.