Quick Answer
The correct combination is Cloud Storage triggers, Cloud Functions, and Vertex AI Pipelines. This works because Cloud Storage triggers fire an event-driven notification when new training data arrives, which invokes a Cloud Function that acts as the lightweight orchestrator to start a Vertex AI Pipeline for automated retraining and deployment. On the Google Professional Data Engineer exam, this scenario tests your understanding of building a fully managed, event-driven retraining pipeline that eliminates manual intervention—a common requirement for production ML CI/CD. A frequent trap is choosing Cloud Composer or Pub/Sub alone, but the key is that Cloud Functions provide the direct serverless bridge between the storage event and the pipeline execution. Memory tip: think “Storage event → Function trigger → Pipeline run” as the three-step chain for automated retraining on new data arrival.
This PDE practice question tests your understanding of operationalizing machine learning models. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A team is implementing CI/CD for their ML models using Google Cloud. They want to automatically retrain and deploy a new model version when new training data arrives in Cloud Storage. Which combination of services should they use?
Cloud Storage triggers, Cloud Functions, and Vertex AI Pipelines
Event-driven pipeline with managed ML services.
Cloud Scheduler and Vertex AI Training
Why wrong: Scheduler runs on cron, not event-driven.
Cloud Pub/Sub and Cloud Composer
Why wrong: Works but more complex than needed.
Cloud Storage notifications and Cloud Build
Why wrong: Cloud Build is not optimized for ML workflows.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Cloud Storage triggers, Cloud Functions, and Vertex AI Pipelines
Option A is correct because Cloud Storage triggers fire an event when new data arrives, which invokes a Cloud Function that can start a Vertex AI Pipeline for retraining and deploying the model. This combination provides a fully managed, event-driven CI/CD pipeline for ML models without manual intervention.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Cloud Storage triggers, Cloud Functions, and Vertex AI Pipelines
Why this is correct
Event-driven pipeline with managed ML services.
Related concept
Read the scenario before looking for a memorised answer.
Cloud Scheduler and Vertex AI Training
Why it's wrong here
Scheduler runs on cron, not event-driven.
Cloud Pub/Sub and Cloud Composer
Why it's wrong here
Works but more complex than needed.
Cloud Storage notifications and Cloud Build
Why it's wrong here
Cloud Build is not optimized for ML workflows.
Common exam traps
Google Cloud often tests the distinction between event-driven triggers (Cloud Storage triggers) and time-based scheduling (Cloud Scheduler), leading candidates to choose B or D when they overlook the need for automatic retraining upon data arrival.
Detailed technical explanation
Cloud Storage triggers use Pub/Sub notifications under the hood, but the Cloud Function abstracts this complexity, allowing direct invocation of Vertex AI Pipelines via the AI Platform API. Vertex AI Pipelines leverages Kubeflow Pipelines to orchestrate containerized training and deployment steps, ensuring reproducibility and scalability. In a real-world scenario, this setup can handle large datasets by triggering a pipeline that preprocesses data, trains a model, and registers it in Vertex AI Model Registry for serving.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
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FAQ
Operationalizing machine learning models — This question tests Operationalizing machine learning models — Read the scenario before looking for a memorised answer..
The correct answer is: Cloud Storage triggers, Cloud Functions, and Vertex AI Pipelines — Option A is correct because Cloud Storage triggers fire an event when new data arrives, which invokes a Cloud Function that can start a Vertex AI Pipeline for retraining and deploying the model. This combination provides a fully managed, event-driven CI/CD pipeline for ML models without manual intervention.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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