PMLE Serving and Scaling Models Practice Question
A startup is deploying a scikit-learn model to Vertex AI for online predictions. They want to minimize the effort required to containerize the model and ensure it can handle HTTP requests. What should they do?
⚠ Common exam trap
The trap here is assuming that a custom container is always needed, overlooking the convenience and compatibility of Vertex AI's pre-built containers for common frameworks.
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 deploy the model by specifying the model artifact in Cloud Storage.
The pre-built scikit-learn container on Vertex AI eliminates the need for custom containerization and HTTP server code. It automatically handles model loading and prediction requests, making it the lowest-effort solution for deploying scikit-learn models.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use the pre-built scikit-learn container provided by Vertex AI and deploy the model by specifying the model artifact in Cloud Storage.
Why this is correct
Vertex AI provides pre-built containers for popular frameworks like scikit-learn. These containers handle HTTP serving, request parsing, and model loading automatically. By simply pointing to the model artifact, the startup avoids containerization effort and ensures compatibility with Vertex AI's prediction API.
- ✗
Convert the scikit-learn model to TensorFlow SavedModel format and use the TensorFlow pre-built container for deployment.
Why it's wrong here
Converting a scikit-learn model to TensorFlow format is non-trivial and may introduce conversion errors or performance differences. It also adds unnecessary complexity. The pre-built scikit-learn container is specifically designed for this use case and requires no conversion.
- ✗
Deploy the model using a custom container that includes the scikit-learn library and a simple HTTP server implemented with Python's http.server module.
Why it's wrong here
Using Python's http.server is not production-ready; it is single-threaded and lacks features like request batching and health checks. Building a custom container also requires more effort than using a pre-built one. This approach may lead to reliability and performance issues.
- ✗
Write a custom Flask application to load the model and expose a /predict endpoint, then build a Docker image and push it to Artifact Registry.
Why it's wrong here
Writing a custom Flask app is more effort than using Vertex AI's pre-built containers. It requires managing dependencies, HTTP server configuration, and error handling. While it offers flexibility, it is not the minimal-effort approach and can introduce inconsistencies with Vertex AI's expected request format.
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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.