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
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Operationalizing machine learning models — study guide chapter
Learn the concepts, then practise the questions
Operationalizing machine learning models practice questions
Targeted practice on this topic area only
All PDE questions
499 questions across all exam domains
Google Professional Data Engineer study guide
Full concept coverage aligned to exam objectives
PDE practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Practise PDE questions linked to Designing data processing systems.
Practise PDE questions linked to Building and operationalizing data processing systems.
Practise PDE questions linked to Operationalizing machine learning models.
Practise PDE questions linked to Ensuring solution quality.
Practise PDE questions linked to PDE fundamentals.
Practise PDE questions linked to PDE scenario.
Practise PDE questions linked to PDE troubleshooting.
Practice this exam
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Keep practising
Last reviewed: Jun 30, 2026
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.