- A
Use Cloud Build to orchestrate the whole process, with a manual approval step before deploying to production.
Cloud Build supports manual approval gates, making it suitable for CD.
- B
Use Vertex AI Pipelines to deploy to production directly after evaluation.
Why wrong: This bypasses manual approval, which is required in the scenario.
- C
Use Cloud Scheduler to trigger deployment every hour.
Why wrong: Not event-driven and lacks evaluation gate.
- D
Use Cloud Functions to deploy after evaluation without approval.
Why wrong: Lacks manual approval step.
Question 894 of 1,000
PMLE Automating and Orchestrating ML Pipelines Practice Question
This PMLE practice question tests your understanding of automating and orchestrating ml pipelines. 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.
A team wants to implement continuous delivery for their ML models. They have a pipeline that trains a model and evaluates it. If the evaluation metrics exceed a threshold, the model should be deployed to a staging endpoint, and after manual approval, to production. Which approach should they use?
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 Cloud Build to orchestrate the whole process, with a manual approval step before deploying to production.
Option A is correct because Cloud Build supports manual approval steps via its 'approval' configuration in the build YAML, allowing the team to gate the production deployment after staging evaluation. This aligns with the requirement for continuous delivery (not deployment) where a human-in-the-loop approves the final production rollout. Vertex AI Pipelines lacks native manual approval gating, and the other options bypass the required manual approval step entirely.
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 Build to orchestrate the whole process, with a manual approval step before deploying to production.
Why this is correct
Cloud Build supports manual approval gates, making it suitable for CD.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Vertex AI Pipelines to deploy to production directly after evaluation.
Why it's wrong here
This bypasses manual approval, which is required in the scenario.
- ✗
Use Cloud Scheduler to trigger deployment every hour.
Why it's wrong here
Not event-driven and lacks evaluation gate.
- ✗
Use Cloud Functions to deploy after evaluation without approval.
Why it's wrong here
Lacks manual approval step.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is confusing continuous delivery (which includes a manual approval gate) with continuous deployment (which is fully automated), leading candidates to choose options that skip the required human approval step.
Trap categories for this question
Scenario analysis trap
This bypasses manual approval, which is required in the scenario.
Detailed technical explanation
How to think about this question
Cloud Build's approval mechanism uses a 'approval' block in the build configuration that pauses the pipeline at a specified step, waiting for a user with appropriate IAM permissions (e.g., roles/cloudbuild.approver) to approve or reject via the gcloud CLI or Cloud Console. This is distinct from continuous deployment, where no human intervention occurs; continuous delivery deliberately includes a manual gate to ensure compliance or risk mitigation. In practice, teams often combine Cloud Build with Cloud Deploy for more advanced delivery strategies like canary or blue/green deployments, but the manual approval step is the key differentiator here.
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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Automating and Orchestrating ML Pipelines — study guide chapter
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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: Use Cloud Build to orchestrate the whole process, with a manual approval step before deploying to production. — Option A is correct because Cloud Build supports manual approval steps via its 'approval' configuration in the build YAML, allowing the team to gate the production deployment after staging evaluation. This aligns with the requirement for continuous delivery (not deployment) where a human-in-the-loop approves the final production rollout. Vertex AI Pipelines lacks native manual approval gating, and the other options bypass the required manual approval step entirely.
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: 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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