PMLE Practice Question: Collaborating Within and Across Teams to Manage Data and Models
Your team trains a scikit-learn model locally and uploads it to Vertex AI Model Registry. A colleague needs to deploy it to a Vertex AI Endpoint for online prediction with a prebuilt container. The model artifacts are stored in a Cloud Storage bucket. Which deployment approach should they use?
⚠ Common exam trap
The trap here is assuming that a locally trained scikit-learn model must be converted to TensorFlow SavedModel or packaged with custom code before it can be deployed to a Vertex AI Endpoint.
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
✓
Import the model with the prebuilt scikit-learn container image and deploy it to an Endpoint using the Model Registry UI or the gcloud ai endpoints deploy-model command.
The scikit-learn model was trained locally and stored in Cloud Storage, so it should be imported into Vertex AI Model Registry using the prebuilt scikit-learn container. This allows direct deployment to a Vertex AI Endpoint for online prediction. The prebuilt container removes the need for custom inference code and supports the standard deployment workflow via console or gcloud CLI.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create a custom prediction routine in a Python source distribution, upload it as a model artifact, and deploy it without specifying a serving container.
Why it's wrong here
A custom prediction routine is supported only when you use a custom container or a prebuilt container that supports custom routines, and you must still specify a serving container at deployment. Simply uploading a Python source distribution without a container will not produce a deployable model in Vertex AI. The scenario calls for a straightforward deployment of a scikit-learn model, not custom inference logic.
- ✗
Export the model to a SavedModel format, import it with a TensorFlow prebuilt container, and deploy it to an Endpoint.
Why it's wrong here
Converting a scikit-learn model to a TensorFlow SavedModel is not a supported or practical conversion path, and the TensorFlow prebuilt container expects TensorFlow SavedModel artifacts. This approach would fail because the model's framework does not match the serving container. The scikit-learn prebuilt container should be used instead to serve the model without conversion.
- ✓
Import the model with the prebuilt scikit-learn container image and deploy it to an Endpoint using the Model Registry UI or the gcloud ai endpoints deploy-model command.
Why this is correct
This is correct because Vertex AI Model Registry supports importing scikit-learn models with a prebuilt serving container, and the imported model can be deployed directly to an Endpoint. The prebuilt scikit-learn container handles prediction requests without requiring custom inference code, and deployment can be done through the console or gcloud CLI. This matches the standard workflow for deploying a locally trained scikit-learn model.
- ✗
Upload the model artifact to Vertex AI Model Registry as a BigQuery ML model and deploy it using the BigQuery ML serving container.
Why it's wrong here
BigQuery ML models are trained and served within BigQuery, and they cannot be imported as generic model artifacts into Vertex AI Model Registry for deployment to an Endpoint with the BigQuery ML serving container. The scikit-learn model was trained outside BigQuery, so this path is invalid. Importing with the scikit-learn prebuilt container is the correct method.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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.