- A
Use Cloud Functions to periodically check for new model versions and deploy.
Why wrong: Periodic polling is inefficient; event-driven deployment via pipeline is better.
- B
Store model versions in Vertex AI Model Registry with version aliases (e.g., 'staging', 'production').
Correct: Model Registry with aliases enables controlled promotion across environments.
- C
Manually approve each model version before deployment.
Why wrong: Manual approval contradicts continuous delivery; automated gates are preferred.
- D
Use Cloud Build to trigger the pipeline on new model code commits.
Correct: CI trigger on code changes is standard for continuous delivery.
- E
Deploy every model version directly to production without evaluation.
Why wrong: Skipping evaluation gate increases risk; not a best practice.
PMLE Automating and Orchestrating ML Pipelines Practice Question
This PMLE practice question tests your understanding of automating and orchestrating ml pipelines. 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 machine learning team uses Vertex AI Pipelines to run a multi-step training pipeline. They want to implement a continuous delivery (CD) process where a model is automatically promoted from staging to production only if it passes an evaluation gate. Which TWO actions should they include in their CI/CD pipeline? (Choose two.)
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
Store model versions in Vertex AI Model Registry with version aliases (e.g., 'staging', 'production').
Option B is correct because Vertex AI Model Registry supports version aliases like 'staging' and 'production', which allow the CI/CD pipeline to automatically promote a model from staging to production only after it passes the evaluation gate. This enables a controlled, automated CD process without manual intervention.
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.
- ✗
Use Cloud Functions to periodically check for new model versions and deploy.
Why it's wrong here
Periodic polling is inefficient; event-driven deployment via pipeline is better.
- ✓
Store model versions in Vertex AI Model Registry with version aliases (e.g., 'staging', 'production').
Why this is correct
Correct: Model Registry with aliases enables controlled promotion across environments.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Manually approve each model version before deployment.
Why it's wrong here
Manual approval contradicts continuous delivery; automated gates are preferred.
- ✓
Use Cloud Build to trigger the pipeline on new model code commits.
Why this is correct
Correct: CI trigger on code changes is standard for continuous delivery.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Deploy every model version directly to production without evaluation.
Why it's wrong here
Skipping evaluation gate increases risk; not a best practice.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between automated promotion using model registry aliases versus manual approval or polling-based triggers, expecting candidates to recognize that version aliases enable seamless, event-driven CD without external polling or human intervention.
Detailed technical explanation
How to think about this question
Vertex AI Model Registry uses version aliases as mutable labels (e.g., 'staging', 'production') that can be reassigned programmatically via the Vertex AI SDK or API. When a model passes evaluation, the pipeline can update the alias from 'staging' to 'production' using the `update_model` method, ensuring zero-downtime promotion. In a real-world scenario, this allows A/B testing or canary deployments by assigning multiple aliases to different model versions.
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
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.
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FAQ
Questions learners often ask
What does this PMLE question test?
Automating and Orchestrating ML Pipelines — This question tests Automating and Orchestrating ML Pipelines — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Store model versions in Vertex AI Model Registry with version aliases (e.g., 'staging', 'production'). — Option B is correct because Vertex AI Model Registry supports version aliases like 'staging' and 'production', which allow the CI/CD pipeline to automatically promote a model from staging to production only after it passes the evaluation gate. This enables a controlled, automated CD process without manual intervention.
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.
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Last reviewed: Jul 4, 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.
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