Quick Answer
The answer is uploading a model to Vertex AI Model Registry. This Cloud Build step executes the `gcloud ai models upload` command, which registers a model artifact’s metadata and storage location within Vertex AI, enabling version control and future deployment without triggering training or creating an endpoint. On the Google Professional Machine Learning Engineer exam, this task tests your understanding of MLOps pipelines where Cloud Build automates model registration after training; a common trap is confusing this step with model deployment or endpoint creation. Remember that “upload” equals “register” in the registry, not “serve.” A useful memory tip: think of the Model Registry as a library catalog—uploading adds the book to the shelf, but you still need a separate deployment step to check it out for reading.
This PMLE practice question tests your understanding of architecting low-code ml solutions. 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.
Refer to the exhibit. What is this Cloud Build step doing?
Uploading a model to Vertex AI Model Registry
The 'upload' command registers the model.
Deploying a model to a Vertex AI endpoint
Why wrong: Deployment uses 'deploy model' command.
Creating a custom container for prediction
Why wrong: The container image is already provided; this step uploads the model artifact.
Training a model in Vertex AI
Why wrong: Training uses 'ai custom-jobs' or training pipelines.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Uploading a model to Vertex AI Model Registry
The Cloud Build step shown uses the `gcloud ai models upload` command, which specifically uploads a model artifact to the Vertex AI Model Registry. This action registers the model metadata and location in Vertex AI, making it available for versioning and later deployment, but does not create an endpoint or perform training.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Uploading a model to Vertex AI Model Registry
Why this is correct
The 'upload' command registers the model.
Related concept
Read the scenario before looking for a memorised answer.
Deploying a model to a Vertex AI endpoint
Why it's wrong here
Deployment uses 'deploy model' command.
Creating a custom container for prediction
Why it's wrong here
The container image is already provided; this step uploads the model artifact.
Training a model in Vertex AI
Why it's wrong here
Training uses 'ai custom-jobs' or training pipelines.
Common exam traps
Google Cloud often tests the distinction between model registration (upload) and model deployment (endpoint creation), leading candidates to confuse the `gcloud ai models upload` step with the actual deployment to an endpoint.
Trap categories for this question
Command / output trap
Deployment uses 'deploy model' command.
Detailed technical explanation
The `gcloud ai models upload` command registers a model in the Vertex AI Model Registry by creating a `Model` resource with metadata such as the artifact URI (typically in Cloud Storage) and the container image for serving. This step is a prerequisite for deploying the model to an endpoint, as the endpoint references a deployed model version from the registry. The registry supports versioning and can store multiple model versions under the same display name, enabling A/B testing and rollback scenarios.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Architecting low-code ML solutions — study guide chapter
Learn the concepts, then practise the questions
Architecting low-code ML solutions 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
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Practise PMLE questions linked to Scaling prototypes into ML models.
Practise PMLE questions linked to Automating and orchestrating ML pipelines.
Practise PMLE questions linked to Collaborating within and across teams to manage data and models.
Practise PMLE questions linked to Architecting low-code ML solutions.
Practise PMLE questions linked to Collaborating to manage data and models.
Practise PMLE questions linked to Serving and scaling models.
Practise PMLE questions linked to Monitoring ML solutions.
Practise PMLE questions linked to Solving business challenges with ML.
Practise PMLE questions linked to PMLE fundamentals.
Practise PMLE questions linked to PMLE scenario.
Practise PMLE questions linked to PMLE troubleshooting.
Practice this exam
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Architecting low-code ML solutions — This question tests Architecting low-code ML solutions — Read the scenario before looking for a memorised answer..
The correct answer is: Uploading a model to Vertex AI Model Registry — The Cloud Build step shown uses the `gcloud ai models upload` command, which specifically uploads a model artifact to the Vertex AI Model Registry. This action registers the model metadata and location in Vertex AI, making it available for versioning and later deployment, but does not create an endpoint or perform training.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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
Last reviewed: Jun 30, 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.