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PDE Practice Question: Which TWO steps are required to deploy a custom…
Which TWO steps are required to deploy a custom scikit-learn model to Vertex AI for online predictions?
⚠ Common exam trap
Google Cloud often tests the misconception that you must always write a custom prediction routine or containerize your model, when in fact Vertex AI provides pre-built containers for popular frameworks like scikit-learn, making steps A and B unnecessary for standard deployments.
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
✓
Save the model using joblib or pickle
Scikit-learn models must be serialized using joblib or pickle to be saved as a model artifact that can be uploaded to Vertex AI. Vertex AI's pre-built prediction containers for scikit-learn expect the model file to be in this format (typically model.joblib or model.pkl) to serve online predictions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Write a custom prediction routine
Why it's wrong here
Pre-built container handles prediction.
- ✗
Containerize the model using Docker
Why it's wrong here
Vertex AI supports pre-built containers for scikit-learn.
- ✓
Save the model using joblib or pickle
Why this is correct
Vertex AI expects a saved model artifact.
- ✗
Create a Vertex AI Endpoint manually
Why it's wrong here
Endpoint can be created during deployment.
- ✓
Upload the model to Vertex AI Model Registry
Why this is correct
Model must be registered before deployment.
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This PDE 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 PDE exam.