- A
Implement a manual approval process where data scientists request dataset paths from the data governance team before each training run.
Why wrong: Manual process is slow and not enforceable at runtime.
- B
After training, run a validation step that checks if the dataset used matches the latest approved version, and roll back if not.
Why wrong: This is reactive and still allows unapproved training to proceed initially.
- C
Use a curated dataset registry in BigQuery or Cloud Storage with IAM conditions that allow access only to datasets tagged as 'approved'. Modify the CI/CD pipeline to pass only approved dataset references to the training job.
This automates governance by restricting training to approved datasets via IAM and pipeline configuration.
- D
Restrict all Cloud Storage buckets to be read-only for the data scientists, and have ML engineers copy approved datasets to a separate bucket.
Why wrong: This blocks data scientists from accessing any data, preventing even approved experimentation.
Quick Answer
The correct approach is to use a curated dataset registry in BigQuery or Cloud Storage with IAM conditions that allow access only to datasets tagged as 'approved', and modify the CI/CD pipeline to pass only approved dataset references to the training job. This solution enforces data governance at the source by leveraging IAM conditions to restrict access based on metadata tags, ensuring that unapproved data cannot be read by the Vertex AI Pipelines training job regardless of what path a data scientist specifies. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of integrating IAM-based access control with CI/CD pipelines to prevent data drift and compliance violations—a common trap is focusing on pipeline-level validation (which can be bypassed) rather than access-level enforcement. The key insight is that governance must be enforced at the storage layer, not just in the pipeline code. Memory tip: "Tag it, lock it, pipeline it"—tag the approved datasets, lock them with IAM conditions, and let the CI/CD pipeline pass only those tags.
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 financial services company uses Vertex AI Pipelines to train and deploy models for fraud detection. The ML team consists of data scientists who develop models and ML engineers who deploy them. They use a CI/CD pipeline with Cloud Build to build and push Docker images to Artifact Registry, then trigger Vertex AI Pipelines. Recently, the team noticed that a model deployed to production was trained on a dataset that had not been approved by the data governance team. Upon investigation, they found that a data scientist accidentally used an unapproved version of the training data by specifying a Cloud Storage path that was not the latest approved dataset. The company needs to enforce that only approved datasets are used in training jobs. Which approach should they take?
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
Use a curated dataset registry in BigQuery or Cloud Storage with IAM conditions that allow access only to datasets tagged as 'approved'. Modify the CI/CD pipeline to pass only approved dataset references to the training job.
Option C is correct because it enforces governance at the source by using IAM conditions to restrict access to only approved datasets, preventing unauthorized data from being used in training. This approach integrates with the CI/CD pipeline to automatically pass only approved dataset references, eliminating the risk of human error in specifying Cloud Storage paths.
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.
- ✗
Implement a manual approval process where data scientists request dataset paths from the data governance team before each training run.
Why it's wrong here
Manual process is slow and not enforceable at runtime.
- ✗
After training, run a validation step that checks if the dataset used matches the latest approved version, and roll back if not.
Why it's wrong here
This is reactive and still allows unapproved training to proceed initially.
- ✓
Use a curated dataset registry in BigQuery or Cloud Storage with IAM conditions that allow access only to datasets tagged as 'approved'. Modify the CI/CD pipeline to pass only approved dataset references to the training job.
Why this is correct
This automates governance by restricting training to approved datasets via IAM and pipeline configuration.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Restrict all Cloud Storage buckets to be read-only for the data scientists, and have ML engineers copy approved datasets to a separate bucket.
Why it's wrong here
This blocks data scientists from accessing any data, preventing even approved experimentation.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between reactive validation (Option B) and proactive enforcement (Option C), where candidates mistakenly choose a post-training check that wastes resources instead of a preventive IAM-based control.
Detailed technical explanation
How to think about this question
Under the hood, IAM conditions on Cloud Storage buckets can use resource tags (e.g., 'data-governance: approved') to allow read access only to objects with that tag, while denying access to unapproved datasets. The CI/CD pipeline can be configured to read dataset references from a curated registry (e.g., BigQuery or a metadata store) that only lists approved datasets, ensuring that the training job receives a valid, governance-compliant path. In a real-world scenario, this prevents data leakage or regulatory violations by enforcing policy at the storage layer, not just at the pipeline level.
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.
- →
Collaborating within and across teams to manage data and models — study guide chapter
Learn the concepts, then practise the questions
- →
Collaborating within and across teams to manage data and models practice questions
Targeted practice on this topic area only
- →
All PMLE questions
506 questions across all exam domains
- →
Google Professional Machine Learning Engineer study guide
Full concept coverage aligned to exam objectives
- →
PMLE practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related PMLE practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Scaling prototypes into ML models practice questions
Practise PMLE questions linked to Scaling prototypes into ML models.
Automating and orchestrating ML pipelines practice questions
Practise PMLE questions linked to Automating and orchestrating ML pipelines.
Collaborating within and across teams to manage data and models practice questions
Practise PMLE questions linked to Collaborating within and across teams to manage data and models.
Architecting low-code ML solutions practice questions
Practise PMLE questions linked to Architecting low-code ML solutions.
Collaborating to manage data and models practice questions
Practise PMLE questions linked to Collaborating to manage data and models.
Serving and scaling models practice questions
Practise PMLE questions linked to Serving and scaling models.
Monitoring ML solutions practice questions
Practise PMLE questions linked to Monitoring ML solutions.
Solving business challenges with ML practice questions
Practise PMLE questions linked to Solving business challenges with ML.
PMLE fundamentals practice questions
Practise PMLE questions linked to PMLE fundamentals.
PMLE scenario practice questions
Practise PMLE questions linked to PMLE scenario.
PMLE troubleshooting practice questions
Practise PMLE questions linked to PMLE troubleshooting.
Practice this exam
Start a free PMLE practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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: Use a curated dataset registry in BigQuery or Cloud Storage with IAM conditions that allow access only to datasets tagged as 'approved'. Modify the CI/CD pipeline to pass only approved dataset references to the training job. — Option C is correct because it enforces governance at the source by using IAM conditions to restrict access to only approved datasets, preventing unauthorized data from being used in training. This approach integrates with the CI/CD pipeline to automatically pass only approved dataset references, eliminating the risk of human error in specifying Cloud Storage paths.
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.
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 →
Last reviewed: Jun 30, 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.
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.