PMLE Collaborating to manage data and models Practice Question
A machine learning team uses Vertex AI Pipelines to orchestrate training workflows. They want to share pipeline runs and artifacts with stakeholders who do not have Google Cloud accounts. What should they do?
⚠ Common exam trap
The trap here is assuming that Vertex AI Pipelines has a built-in public sharing URL or that public bucket access is acceptable, when the correct approach is to generate a static report for external sharing.
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
✓
Generate a pipeline run report using the Vertex AI Pipelines SDK and export it as a static HTML file to share.
Exporting a pipeline run report as a static HTML file using the Vertex AI Pipelines SDK enables sharing with stakeholders who lack Google Cloud accounts. This method provides a comprehensive view of the run's artifacts and metrics without requiring access to the cloud environment, ensuring both security and accessibility.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Generate a pipeline run report using the Vertex AI Pipelines SDK and export it as a static HTML file to share.
Why this is correct
The Vertex AI Pipelines SDK allows generating a detailed report of a pipeline run, including artifacts and metrics, which can be exported as a static HTML file. This file can be shared with anyone, regardless of Google Cloud account, providing a snapshot of the run. It is a secure and straightforward way to share information with external stakeholders.
- ✗
Use Vertex AI Pipelines' built-in sharing feature to generate a public URL for the pipeline run.
Why it's wrong here
Vertex AI Pipelines does not have a built-in public sharing feature that generates URLs for unauthenticated users. Sharing typically requires IAM permissions within Google Cloud. This option misrepresents the service's capabilities and would not work for stakeholders without accounts.
- ✗
Export the pipeline run's metadata and artifacts to a Cloud Storage bucket and grant public access.
Why it's wrong here
Granting public access to a Cloud Storage bucket exposes sensitive data and violates security best practices. While it allows sharing, it does not provide a user-friendly interface for stakeholders and lacks access control. This approach is insecure and not recommended for collaboration with external parties.
- ✗
Create a custom dashboard in Looker Studio that reads from Vertex ML Metadata and share it with the stakeholders.
Why it's wrong here
Looker Studio can visualize data, but it requires the stakeholders to have access to the underlying data source, which may still need Google accounts. Additionally, setting up a custom dashboard is not a native sharing mechanism for Vertex AI Pipelines and adds complexity. It does not directly share pipeline runs or artifacts.
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JA
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.