- A
Cloud Composer to orchestrate, with Cloud Storage for libraries.
Why wrong: Cloud Storage for libraries is not as secure as Artifact Registry.
- B
Vertex AI Pipelines with a scheduled trigger, and use Cloud Build to pull libraries from Artifact Registry.
Scheduled pipeline can query BigQuery for new data, and Cloud Build ensures consistent library versions.
- C
Cloud Functions triggered by BigQuery, Cloud Build to run training, and Artifact Registry for libraries.
Why wrong: BigQuery does not naturally trigger Cloud Functions on new data without additional setup.
- D
Vertex AI Experiments with continuous evaluation, and a Cloud Run job for training.
Why wrong: Continuous evaluation is for model monitoring, not trigger.
- E
Dataflow to preprocess, then trigger a Cloud Run job.
Why wrong: Dataflow handles preprocessing but not library enforcement.
Quick Answer
The answer is Vertex AI Pipelines with a scheduled trigger combined with Cloud Build to pull libraries from Artifact Registry. This combination is correct because Vertex AI Pipelines provides a managed orchestration service that can be triggered automatically via Cloud Scheduler or Eventarc when new labeled data lands in BigQuery, while Cloud Build acts as the secure execution layer that pulls only approved, vetted dependencies from Artifact Registry, enforcing compliance without manual intervention. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of integrating MLOps components—specifically how to automate ML retraining while maintaining governance over library usage. A common trap is choosing a simple Cloud Function trigger without considering the need for approved libraries, or assuming Vertex AI Training alone handles dependency management. Remember the pairing: orchestration needs Pipelines, library control needs Cloud Build—think “Pipeline for flow, Build for approval.”
PMLE Practice Question: Collaborating within and across teams to manage data and models
This PMLE practice question tests your understanding of collaborating within and across teams to manage data and 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 uses Vertex AI Experiments to track ML training runs. They want to automatically trigger a retraining pipeline when new labeled data arrives in BigQuery, and ensure the pipeline uses only approved libraries from a central artifact registry. Which combination of services should they use?
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 Pipelines with a scheduled trigger, and use Cloud Build to pull libraries from Artifact Registry.
Option B is correct because Vertex AI Pipelines provides a managed orchestration service for ML workflows, and a scheduled trigger can be set to run the pipeline when new labeled data arrives in BigQuery (e.g., via a Cloud Scheduler or Eventarc trigger). Cloud Build is used to pull approved libraries from Artifact Registry, ensuring only vetted dependencies are used during pipeline execution, which meets the security and compliance requirement.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cloud Composer to orchestrate, with Cloud Storage for libraries.
Why it's wrong here
Cloud Storage for libraries is not as secure as Artifact Registry.
- ✓
Vertex AI Pipelines with a scheduled trigger, and use Cloud Build to pull libraries from Artifact Registry.
Why this is correct
Scheduled pipeline can query BigQuery for new data, and Cloud Build ensures consistent library versions.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Cloud Functions triggered by BigQuery, Cloud Build to run training, and Artifact Registry for libraries.
Why it's wrong here
BigQuery does not naturally trigger Cloud Functions on new data without additional setup.
- ✗
Vertex AI Experiments with continuous evaluation, and a Cloud Run job for training.
Why it's wrong here
Continuous evaluation is for model monitoring, not trigger.
- ✗
Dataflow to preprocess, then trigger a Cloud Run job.
Why it's wrong here
Dataflow handles preprocessing but not library enforcement.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse Cloud Build (a CI/CD service) with Vertex AI Training (a managed ML training service), or think that Cloud Composer is the only orchestration option for ML pipelines, when Vertex AI Pipelines is the native, more integrated choice for ML workflows on Vertex AI.
Detailed technical explanation
How to think about this question
Vertex AI Pipelines uses Kubeflow Pipelines SDK or the Google Cloud Pipeline Components to define a DAG of steps, each running in a container. The scheduled trigger can be implemented via Cloud Scheduler or Eventarc, which listens for BigQuery insert events (e.g., using BigQuery's table update events via Pub/Sub). Artifact Registry stores container images and Python packages; Cloud Build can be used as a step within the pipeline to pull specific versions of libraries from Artifact Registry, ensuring reproducibility and compliance with organizational policies.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this PMLE question test?
Collaborating within and across teams to manage data and models — This question tests Collaborating within and across teams to manage data and models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Vertex AI Pipelines with a scheduled trigger, and use Cloud Build to pull libraries from Artifact Registry. — Option B is correct because Vertex AI Pipelines provides a managed orchestration service for ML workflows, and a scheduled trigger can be set to run the pipeline when new labeled data arrives in BigQuery (e.g., via a Cloud Scheduler or Eventarc trigger). Cloud Build is used to pull approved libraries from Artifact Registry, ensuring only vetted dependencies are used during pipeline execution, which meets the security and compliance requirement.
What should I do if I get this PMLE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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.
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