- A
1. Build and push the container, 2. Register the model, 3. Deploy to an endpoint, 4. Test
This is the correct order because you first create the container image, then register it as a model in Vertex AI, then deploy it to an endpoint, and finally test the live endpoint.
- B
1. Register the model, 2. Build and push the container, 3. Deploy to an endpoint, 4. Test
Why wrong: This is incorrect because you cannot register a model before building and pushing its container image; the model resource depends on the container.
- C
1. Build and push the container, 2. Deploy to an endpoint, 3. Register the model, 4. Test
Why wrong: This is incorrect because you must register the model in Vertex AI before deploying it; deployment requires a registered model version.
- D
1. Build and push the container, 2. Register the model, 3. Test, 4. Deploy to an endpoint
Why wrong: This is incorrect because testing occurs after deployment, not before; you need an endpoint to send prediction requests to.
Vertex AI Custom Container Deployment Steps
This PMLE practice question tests your understanding of vertex ai model registry. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. A key principle to apply: vertex AI Model Registry. 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.
Drag and drop the steps to create and deploy a custom ML model on Vertex AI using a container in the correct order.
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
1. Build and push the container, 2. Register the model, 3. Deploy to an endpoint, 4. Test
The correct order to create and deploy a custom ML model on Vertex AI using a container is: First, build and push the container image to Artifact Registry. Next, register the model in Vertex AI Model Registry. Then, deploy the model to an endpoint using Vertex AI Endpoints. Finally, test the endpoint by sending prediction requests to verify it works.
Key principle: Vertex AI Model Registry
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
1. Build and push the container, 2. Register the model, 3. Deploy to an endpoint, 4. Test
Why this is correct
This is the correct order because you first create the container image, then register it as a model in Vertex AI, then deploy it to an endpoint, and finally test the live endpoint.
Related concept
Vertex AI Model Registry
- ✗
1. Register the model, 2. Build and push the container, 3. Deploy to an endpoint, 4. Test
Why it's wrong here
This is incorrect because you cannot register a model before building and pushing its container image; the model resource depends on the container.
- ✗
1. Build and push the container, 2. Deploy to an endpoint, 3. Register the model, 4. Test
Why it's wrong here
This is incorrect because you must register the model in Vertex AI before deploying it; deployment requires a registered model version.
- ✗
1. Build and push the container, 2. Register the model, 3. Test, 4. Deploy to an endpoint
Why it's wrong here
This is incorrect because testing occurs after deployment, not before; you need an endpoint to send prediction requests to.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Vertex AI Model Registry
- Vertex AI Endpoints
- Container Registry
- Custom container
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
Vertex AI Model Registry
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. Vertex AI Model Registry 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.
Review vertex AI Model Registry, then practise related PMLE questions on the same topic to reinforce the concept.
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.
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.
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.
Architecting Low-Code ML Solutions practice questions
Practise PMLE questions linked to Architecting Low-Code ML Solutions.
Scaling Prototypes into ML Models practice questions
Practise PMLE questions linked to Scaling Prototypes into ML Models.
Collaborating to manage data and models practice questions
Practise PMLE questions linked to Collaborating to manage data and models.
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?
Vertex AI Model Registry
What is the correct answer to this question?
The correct answer is: 1. Build and push the container, 2. Register the model, 3. Deploy to an endpoint, 4. Test — The correct order to create and deploy a custom ML model on Vertex AI using a container is: First, build and push the container image to Artifact Registry. Next, register the model in Vertex AI Model Registry. Then, deploy the model to an endpoint using Vertex AI Endpoints. Finally, test the endpoint by sending prediction requests to verify it works.
What should I do if I get this PMLE question wrong?
Review vertex AI Model Registry, then practise related PMLE questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Vertex AI Model Registry
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.