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PMLE Practice Question: A financial services company uses Vertex AI…
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?
⚠ Common exam trap
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.
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.
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.
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.
- ✗
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.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.