easyMultiple Choice
PDE Practice Question: A data science team has built a model using…
A data science team has built a model using scikit-learn. They want to operationalize it on Google Cloud without rewriting the code. Which approach should they take?
⚠ Common exam trap
Google Cloud often tests the misconception that AI Platform Training can host models directly, but it is strictly for training jobs, not serving endpoints; candidates confuse the training service with the prediction service.
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
✓
Package the model in a custom container and deploy to Vertex AI Endpoints
Vertex AI Endpoints support custom containers, allowing you to package your scikit-learn model with its dependencies (e.g., a Flask or FastAPI inference server) and deploy it without rewriting any code. This approach directly meets the requirement to operationalize the existing model on Google Cloud without modification.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Export the model as a PMML file and use BigQuery ML
Why it's wrong here
BigQuery ML trains and predicts with its own supported model types; it cannot import PMML, so the scikit-learn code would effectively be rewritten. It is tempting because BigQuery ML keeps prediction inside SQL, and it would be correct if the model were built natively there.
- ✗
Use AI Platform Training to host the model directly
Why it's wrong here
AI Platform Training runs training jobs and outputs artefacts; it does not serve scikit-learn models for online prediction. It is tempting because it hosts the training workload, and it would be correct if the team needed distributed training rather than deploying the already-built model.
- ✓
Package the model in a custom container and deploy to Vertex AI Endpoints
Why this is correct
Custom containers preserve the existing scikit-learn code and dependencies unchanged, satisfying the no-rewrite constraint. Vertex AI Endpoints accepts any container implementing the prediction server contract, so the team packages their model and serves it without porting to a native framework.
- ✗
Convert the scikit-learn model to TensorFlow SavedModel format
Why it's wrong here
Converting to TensorFlow SavedModel requires reimplementing the scikit-learn model in TensorFlow, which is rewriting the code the team wants to avoid. It is tempting because SavedModel is a portable serving format, and it would be correct if the model were already authored in TensorFlow.
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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.