PMLE Serving and Scaling Models Practice Question
You have trained a scikit-learn model and saved it as a joblib file in Cloud Storage. You need to deploy this model to Vertex AI for online predictions with minimal effort. What should you do?
⚠ Common exam trap
The trap here is assuming that you need to write a custom container or convert the model format to deploy a scikit-learn model on Vertex AI.
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
✓
Use the pre-built scikit-learn container provided by Vertex AI and specify the model artifact path in Cloud Storage.
Vertex AI offers pre-built containers for scikit-learn that can load joblib files directly. By using the pre-built container and specifying the model artifact path, you can deploy the model with minimal effort. Writing a custom container or converting the model format adds unnecessary work, and Model Registry does not support containerless deployment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Upload the joblib file to Vertex AI Model Registry and deploy it directly without specifying a container.
Why it's wrong here
Vertex AI Model Registry requires you to specify a container image when importing a model. You cannot deploy a model without specifying a serving container. The pre-built scikit-learn container is the appropriate choice, but you must explicitly provide it.
- ✗
Convert the scikit-learn model to TensorFlow SavedModel format and use the pre-built TensorFlow container.
Why it's wrong here
Converting a scikit-learn model to TensorFlow is non-trivial and unnecessary. Vertex AI provides a pre-built scikit-learn container that can directly load joblib files. Converting the model adds complexity and potential errors, contradicting the goal of minimal effort.
- ✗
Write a custom container that loads the joblib file and serves predictions using Flask, then deploy it to Vertex AI.
Why it's wrong here
While a custom container gives you full control, it requires writing and maintaining a serving application, which is more effort than using a pre-built container. The requirement is minimal effort, so a custom container is not the best choice here.
- ✓
Use the pre-built scikit-learn container provided by Vertex AI and specify the model artifact path in Cloud Storage.
Why this is correct
Vertex AI provides pre-built containers for popular frameworks like scikit-learn. You can deploy the model by specifying the pre-built container image and the path to the joblib file in Cloud Storage. This requires no custom code and is the fastest way to deploy a scikit-learn model for online predictions.
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JA
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.