- A
Deploy each model version to a separate endpoint, and use a custom database to track which endpoint is 'production'. Then use migration scripts to switch traffic.
Why wrong: Creates complexity and does not use Vertex AI's built-in versioning; prone to errors.
- B
Store the model version metadata in a BigQuery table and use a scheduled query to automatically update the endpoint deployment based on validation results.
Why wrong: Does not integrate with Model Registry versioning; risky and not scalable.
- C
Use Vertex AI Model Registry labels to mark versions as 'staging' or 'production', and create a Cloud Function that checks the label before deploying to the endpoint.
Why wrong: Labels are not designed for workflow state and can be changed arbitrarily without audit.
- D
Use Vertex AI Model Registry version aliases ('staging', 'production') and configure Cloud Build to trigger a Cloud Run service that handles approval logic, then update the alias upon approval.
Version aliases provide a built-in way to denote environment stages and can be updated programmatically after validation and approval.
Quick Answer
The answer is to use Vertex AI Model Registry version aliases like 'staging' and 'production', combined with a Cloud Build-triggered Cloud Run service for approval logic. This is correct because version aliases natively track lifecycle stages within the registry, allowing you to promote a model version by simply updating its alias after passing validation gates. The Cloud Run service acts as a lightweight approval handler, enforcing reviewer sign-off before the alias update occurs, which eliminates the need for custom databases or manual scripts. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of how to implement CI/CD governance without overcomplicating infrastructure—a common trap is reaching for external approval systems like Cloud Tasks or Pub/Sub when the registry’s alias system already provides the necessary state tracking. Remember the key insight: aliases are not just labels; they are the promotion mechanism itself. Memory tip: think of aliases as “stage tags” that you move, not copy—promotion is just a tag reassignment.
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. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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.
Your team manages multiple ML models in Vertex AI Model Registry. Each model has several versions deployed to different endpoints for testing and production. You need to implement a process where a model version can be promoted from a staging environment to production only after it has passed automated validation tests and been approved by a designated reviewer. The team uses CI/CD pipelines (Cloud Build) for training and deployment. Currently, model versions are deployed to endpoints using Vertex AI Endpoints with a single traffic split configuration. You want to track promotion requests and enforce approval gates. 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
Use Vertex AI Model Registry version aliases ('staging', 'production') and configure Cloud Build to trigger a Cloud Run service that handles approval logic, then update the alias upon approval.
Option D is correct because Vertex AI Model Registry version aliases (e.g., 'staging', 'production') are designed to track model version lifecycle stages. By integrating Cloud Build to trigger a Cloud Run service that enforces approval logic before updating the alias, you create a clear promotion gate. This approach natively supports tracking promotion requests and enforcing approval without custom databases or manual scripts, aligning with CI/CD best practices.
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.
- ✗
Deploy each model version to a separate endpoint, and use a custom database to track which endpoint is 'production'. Then use migration scripts to switch traffic.
Why it's wrong here
Creates complexity and does not use Vertex AI's built-in versioning; prone to errors.
- ✗
Store the model version metadata in a BigQuery table and use a scheduled query to automatically update the endpoint deployment based on validation results.
Why it's wrong here
Does not integrate with Model Registry versioning; risky and not scalable.
- ✗
Use Vertex AI Model Registry labels to mark versions as 'staging' or 'production', and create a Cloud Function that checks the label before deploying to the endpoint.
Why it's wrong here
Labels are not designed for workflow state and can be changed arbitrarily without audit.
- ✓
Use Vertex AI Model Registry version aliases ('staging', 'production') and configure Cloud Build to trigger a Cloud Run service that handles approval logic, then update the alias upon approval.
Why this is correct
Version aliases provide a built-in way to denote environment stages and can be updated programmatically after validation and approval.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between labels (key-value metadata) and aliases (semantic lifecycle tags) in Vertex AI Model Registry, leading candidates to choose Option C because they confuse labels with the built-in promotion mechanism that aliases provide.
Detailed technical explanation
How to think about this question
Vertex AI Model Registry version aliases are managed via the `aliases` field in the model version resource, allowing you to assign semantic tags like 'staging' or 'production'. When you update an alias, the registry automatically reassigns it to the new version, and endpoints can be configured to serve traffic based on aliases rather than specific version IDs. This enables a clean promotion pipeline where a Cloud Run service can validate approval status (e.g., via a Pub/Sub message from Cloud Build) before calling `projects.locations.models.versions.update` to move the alias, ensuring atomicity and auditability.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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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: Use Vertex AI Model Registry version aliases ('staging', 'production') and configure Cloud Build to trigger a Cloud Run service that handles approval logic, then update the alias upon approval. — Option D is correct because Vertex AI Model Registry version aliases (e.g., 'staging', 'production') are designed to track model version lifecycle stages. By integrating Cloud Build to trigger a Cloud Run service that enforces approval logic before updating the alias, you create a clear promotion gate. This approach natively supports tracking promotion requests and enforcing approval without custom databases or manual scripts, aligning with CI/CD best practices.
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
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Last reviewed: Jun 30, 2026
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