- A
Use Cloud Composer to orchestrate the deployment and add a sensor that waits for approval from the ticketing system via a custom operator.
Why wrong: This adds unnecessary complexity; Cloud Deploy is simpler and more appropriate.
- B
Use Cloud Build's built-in approval gate feature to require compliance team sign-off before deployment.
Why wrong: Cloud Build does not have a built-in approval gate; Cloud Deploy does.
- C
Modify the CI/CD pipeline to use Cloud Deploy's approval gate feature, requiring a manual approval from the compliance team before the deployment step.
Cloud Deploy supports manual approval gates integrated with the pipeline.
- D
Store the model artifacts in Cloud Storage and have the compliance team deploy manually using the gcloud command.
Why wrong: Manual deployment defeats the purpose of automation and is error-prone.
PMLE Collaborating to manage data and models Practice Question
This PMLE practice question tests your understanding of collaborating to manage data and models. 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 financial services company uses Vertex AI to deploy multiple models for fraud detection. The ML team has set up a CI/CD pipeline using Cloud Build and Cloud Deploy. The pipeline builds a custom container with the trained model, pushes it to Artifact Registry, and deploys it to a Vertex AI Endpoint. Recently, a new regulation requires that all model deployments be audited and approved by the compliance team before going live. The compliance team wants to review the model's evaluation metrics and approve the deployment via a ticketing system. Currently, the CI/CD pipeline automatically deploys after the container is built. The team needs to implement a gating process without slowing down the development cycle. What should they 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
Modify the CI/CD pipeline to use Cloud Deploy's approval gate feature, requiring a manual approval from the compliance team before the deployment step.
Option C is correct because Cloud Deploy provides a native approval gate feature that can be inserted into a delivery pipeline to require manual sign-off before a deployment proceeds. This allows the compliance team to review model evaluation metrics and approve via a ticketing system without modifying the CI/CD pipeline's build process, thus maintaining development velocity. The approval gate pauses the deployment at a specific stage, waiting for an external approval signal, which integrates seamlessly with Cloud Deploy's rollout management.
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 Composer to orchestrate the deployment and add a sensor that waits for approval from the ticketing system via a custom operator.
Why it's wrong here
This adds unnecessary complexity; Cloud Deploy is simpler and more appropriate.
- ✗
Use Cloud Build's built-in approval gate feature to require compliance team sign-off before deployment.
Why it's wrong here
Cloud Build does not have a built-in approval gate; Cloud Deploy does.
- ✓
Modify the CI/CD pipeline to use Cloud Deploy's approval gate feature, requiring a manual approval from the compliance team before the deployment step.
Why this is correct
Cloud Deploy supports manual approval gates integrated with the pipeline.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Store the model artifacts in Cloud Storage and have the compliance team deploy manually using the gcloud command.
Why it's wrong here
Manual deployment defeats the purpose of automation and is error-prone.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is confusing Cloud Build's approval gates (which operate at the build stage) with Cloud Deploy's approval gates (which operate at the deployment stage), leading candidates to incorrectly select Option B despite it not addressing the deployment gating requirement.
Detailed technical explanation
How to think about this question
Cloud Deploy's approval gate works by defining a 'deploy' stage in the delivery pipeline that includes a 'requireApproval' flag; when set to true, the rollout pauses at that stage until a user with the appropriate IAM role (e.g., clouddeploy.approver) approves or rejects it via the Cloud Console, gcloud, or API. This mechanism uses Cloud Deploy's rollout lifecycle, where each rollout progresses through phases (e.g., 'Promote', 'Deploy'), and the approval gate blocks the transition to the next phase. In practice, the compliance team can trigger approval via a webhook from their ticketing system, ensuring audit trails are maintained without manual intervention in the CI/CD pipeline.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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.
- →
Collaborating to manage data and models — study guide chapter
Learn the concepts, then practise the questions
- →
Collaborating to manage data and models practice questions
Targeted practice on this topic area only
- →
All PMLE questions
506 questions across all exam domains
- →
Google Professional Machine Learning Engineer study guide
Full concept coverage aligned to exam objectives
- →
PMLE practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related PMLE practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Scaling prototypes into ML models practice questions
Practise PMLE questions linked to Scaling prototypes into ML models.
Automating and orchestrating ML pipelines practice questions
Practise PMLE questions linked to Automating and orchestrating ML pipelines.
Collaborating within and across teams to manage data and models practice questions
Practise PMLE questions linked to Collaborating within and across teams to manage data and models.
Architecting low-code ML solutions practice questions
Practise PMLE questions linked to Architecting low-code ML solutions.
Collaborating to manage data and models practice questions
Practise PMLE questions linked to Collaborating to manage data and models.
Serving and scaling models practice questions
Practise PMLE questions linked to Serving and scaling models.
Monitoring ML solutions practice questions
Practise PMLE questions linked to Monitoring ML solutions.
Solving business challenges with ML practice questions
Practise PMLE questions linked to Solving business challenges with ML.
PMLE fundamentals practice questions
Practise PMLE questions linked to PMLE fundamentals.
PMLE scenario practice questions
Practise PMLE questions linked to PMLE scenario.
PMLE troubleshooting practice questions
Practise PMLE questions linked to PMLE troubleshooting.
Practice this exam
Start a free PMLE practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this PMLE question test?
Collaborating to manage data and models — This question tests Collaborating 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: Modify the CI/CD pipeline to use Cloud Deploy's approval gate feature, requiring a manual approval from the compliance team before the deployment step. — Option C is correct because Cloud Deploy provides a native approval gate feature that can be inserted into a delivery pipeline to require manual sign-off before a deployment proceeds. This allows the compliance team to review model evaluation metrics and approve via a ticketing system without modifying the CI/CD pipeline's build process, thus maintaining development velocity. The approval gate pauses the deployment at a specific stage, waiting for an external approval signal, which integrates seamlessly with Cloud Deploy's rollout management.
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
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Keep practising
More PMLE practice questions
- A travel booking company has a real-time recommendation system that suggests hotels and flights to users. The model is s…
- A global retail company uses Vertex AI Recommendations to provide product recommendations on their website. They have a…
- Your team is developing a machine learning model for real-time fraud detection. The training pipeline runs on Vertex AI…
- A healthcare organization is building a machine learning model to predict patient readmission risk. They have sensitive…
- You are an ML engineer at a global e-commerce company. Your team has developed a deep learning model for product recomme…
- A financial services company uses Vertex AI AutoML Tables to build a credit risk model. The dataset contains 500,000 row…
Last reviewed: Jun 11, 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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.