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PMLE Practice Question: A data scientist has trained a scikit-learn model…
A data scientist has trained a scikit-learn model locally and wants to deploy it to Vertex AI for online predictions with low latency. The model is a small RandomForestClassifier (100 MB). What is the recommended way to deploy this model?
⚠ Common exam trap
Google Cloud often tests the misconception that any model must be containerized or converted to TensorFlow for deployment, but the correct answer leverages the platform's pre-built container for the specific framework, which is the simplest and most efficient path for small models.
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
✓
Upload the model to Vertex AI Model Registry using the pre-built scikit-learn serving container.
Vertex AI provides a pre-built container for scikit-learn that is optimized for serving predictions with low latency. For a small RandomForestClassifier (100 MB), this container handles model loading, request routing, and scaling automatically, eliminating the need for custom infrastructure. This is the recommended approach for deploying scikit-learn models to Vertex AI for 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.
- ✗
Deploy the model on a Kubernetes cluster with Istio.
Why it's wrong here
Vertex AI hosts models on managed prediction infrastructure; a self-managed Kubernetes cluster with Istio adds control-plane and service-mesh overhead without providing the required Vertex AI online prediction endpoint. Kubernetes suits portable, multi-cloud serving where you own the orchestration layer, not a 100 MB scikit-learn model needing low-latency Vertex AI predictions.
- ✗
Package the model as a Docker container with a custom prediction routine.
Why it's wrong here
Vertex AI's pre-built scikit-learn serving container already loads a saved model, so a custom Docker container with a prediction routine adds image-building and registry maintenance without benefit. Custom containers are warranted when preprocessing or dependencies fall outside the supported framework versions, not for a standard 100 MB RandomForestClassifier.
- ✓
Upload the model to Vertex AI Model Registry using the pre-built scikit-learn serving container.
Why this is correct
Vertex AI's pre-built scikit-learn container already implements the prediction server and model-loading contract, so uploading the 100 MB artefact to the Model Registry avoids writing custom serving code and satisfies the low-latency online prediction requirement.
- ✗
Export the model as a TensorFlow SavedModel and use the pre-built TF serving container.
Why it's wrong here
A scikit-learn RandomForestClassifier is not a TensorFlow graph, so exporting it as a SavedModel requires conversion that changes the serialisation format and risks prediction parity. TensorFlow SavedModel with the TF serving container is correct for native TensorFlow models, not for scikit-learn estimators.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.