The answer is to upload the model to Vertex AI Model Registry and use it for deployment. This is correct because the Model Registry serves as the central, version-controlled hub for managing ML models, directly integrating with Vertex AI’s native deployment endpoints and CI/CD pipelines through the SDK or Cloud Build. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of the recommended production workflow: prototyping in Workbench, then promoting the model artifact to the Registry for automated staging and deployment, rather than manually packaging or exporting files. A common trap is choosing a manual export option like saving a pickle file, which bypasses versioning and audit trails. Remember the key chain: Workbench to Registry to Endpoint—the Registry is the bridge that turns a notebook experiment into a deployable, governed asset.
PMLE Scaling prototypes into ML models Practice Question
This PMLE practice question tests your understanding of scaling prototypes into ml 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 team prototypes a recommendation model using a Jupyter notebook on Vertex AI Workbench. They want to productionize the model with CI/CD. Which approach should they use to package the model for deployment?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Upload the model to Vertex AI Model Registry and use it for deployment
Vertex AI Model Registry is the central repository for managing ML models, enabling versioning, evaluation, and deployment to endpoints. This approach integrates with CI/CD pipelines via the Vertex AI SDK or Cloud Build, allowing automated model promotion and deployment without manual packaging. Option D directly leverages Vertex AI's native deployment workflow, which is the recommended path for productionizing models from Workbench.
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 deploy the notebook directly as a prediction endpoint
Why it's wrong here
Notebooks are not directly deployable; need to export the model.
✗
Store the model in Cloud Source Repositories and deploy from there
Why it's wrong here
Source Repositories is for code, not model artifacts.
✗
Containerize the model and push to Artifact Registry, then deploy via Cloud Run
Why it's wrong here
Possible but lacks model versioning and monitoring integration.
✓
Upload the model to Vertex AI Model Registry and use it for deployment
Why this is correct
Model Registry manages versions and deployment targets.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that any storage or code repository (like Cloud Source Repositories or Artifact Registry) can directly serve as a deployment mechanism, when in fact Vertex AI Model Registry is the required service for managing and deploying models within Vertex AI's ecosystem.
Detailed technical explanation
How to think about this question
Vertex AI Model Registry stores model artifacts along with metadata (e.g., framework, version, evaluation metrics) and supports automated deployment to endpoints via the `aiplatform.Model` class or Cloud Build triggers. Under the hood, the registry uses a gRPC-based API to manage model versions and can automatically scale endpoints using custom machine types or GPUs. In a real-world CI/CD pipeline, a model trained in Workbench can be exported to a Cloud Storage bucket, registered via the Vertex AI SDK, and then promoted to an endpoint using a Cloud Build step that calls `model.deploy()`.
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.
Scaling prototypes into ML models — This question tests Scaling prototypes into ML models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Upload the model to Vertex AI Model Registry and use it for deployment — Vertex AI Model Registry is the central repository for managing ML models, enabling versioning, evaluation, and deployment to endpoints. This approach integrates with CI/CD pipelines via the Vertex AI SDK or Cloud Build, allowing automated model promotion and deployment without manual packaging. Option D directly leverages Vertex AI's native deployment workflow, which is the recommended path for productionizing models from Workbench.
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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