hardMultiple ChoiceObjective-mapped
PDE Practice Question: A data engineer at a financial services company…
You are a data engineer at a financial services company that uses Vertex AI to train and deploy models for credit risk assessment. The company has strict governance requirements: every model version must be approved by the risk committee before going to production. The approval process can take several days. Currently, the team trains a new model weekly and manually deploys it to a staging endpoint for review, then manually promotes to production after approval. This process is error-prone and slow. You want to automate the pipeline: training should trigger automatically when new data arrives, the model should be automatically deployed to a staging endpoint for review, and after manual approval, it should be promoted to production. Additionally, you need to ensure that if a model in staging performs poorly (e.g., low accuracy), it should not be promoted even if approved. What should you do?
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
✓
Create a Vertex AI Pipeline that: (1) Triggers on new data, (2) Trains model, (3) Evaluates and stores metrics in the model registry, (4) Deploys to staging endpoint as a new model version. Then use a manual approval step (e.g., via Cloud Build approval or external system) to trigger a second pipeline that checks the stored metrics and, if acceptable, deploys to production endpoint.
The best approach uses Vertex AI Pipelines to automatically train and deploy to a staging endpoint. After manual approval, a separate pipeline step checks model performance metrics (which were stored during training/evaluation) and if they meet a threshold, promotes to production. This enforces governance and automation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Vertex AI Experiments to track model versions, then manually deploy from the Experiments UI.
Why it's wrong here
This is manual and does not automate the pipeline.
- ✗
Use Cloud Scheduler to run training weekly, then use Cloud Functions to deploy to staging, and after manual approval, use another Cloud Function to check performance and deploy to production.
Why it's wrong here
This lacks integration and pipeline orchestration; manual steps are still separate, and performance check should be automated in the pipeline.
- ✓
Create a Vertex AI Pipeline that: (1) Triggers on new data, (2) Trains model, (3) Evaluates and stores metrics in the model registry, (4) Deploys to staging endpoint as a new model version. Then use a manual approval step (e.g., via Cloud Build approval or external system) to trigger a second pipeline that checks the stored metrics and, if acceptable, deploys to production endpoint.
Why this is correct
This automates training and staging deployment, then separates approval gate, and uses metric check to conditionally promote to production.
- ✗
Train models on Vertex AI Workbench and use a CI/CD tool like Cloud Build to deploy to staging. Use a Cloud Build approval step to promote to production after manual check.
Why it's wrong here
Workbench is for notebooks, not automated pipelines; the process is not fully automated and lacks metric check in the promotion step.
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