Google Cloud Professional Machine Learning Engineer — Question 247

You recently trained an XGBoost model on tabular data. You plan to expose the model for internal use as an HTTP microservice. After deployment, you expect a small number of incoming requests. You want to productionize the model with the least amount of effort and latency. What should you do?

Answer options

• A. Deploy the model to BigQuery ML by using CREATE MODEL with the BOOSTED_TREE_REGRESSOR statement, and invoke the BigQuery API from the microservice.
• B. Build a Flask-based app. Package the app in a custom container on Vertex AI, and deploy it to Vertex AI Endpoints.
• C. Build a Flask-based app. Package the app in a Docker image, and deploy it to Google Kubernetes Engine in Autopilot mode.
• D. Use a prebuilt XGBoost Vertex container to create a model, and deploy it to Vertex AI Endpoints.

Correct answer: D

Explanation

The correct answer is D because using a prebuilt XGBoost Vertex container allows for quick deployment with minimal configuration, which is ideal for productionizing the model efficiently. Options A and B require more complex setups with BigQuery API and custom app development, respectively, while option C involves managing Kubernetes, which adds unnecessary overhead for a small number of requests.