Free PROFESSIONAL-MACHINE-LEARNING-ENGINEER Web TestEngine Demo

Q23 Single choice

You recently trained a XGBoost model that you plan to deploy to production for online inference. Before sending a predict request to your model's binary, you need to perform a simple data preprocessing step.
This step exposes a REST API that accepts requests in your internal VPC Service Controls and returns predictions. You want to configure this preprocessing step while minimizing cost and effort.

What should you do?

A
Store a pickled model in Cloud Storage. Build a Flask-based app, package the app in a custom
container image, and deploy the model to Vertex AI Endpoints.
B
Build a Flask-based app, package the app and a pickled model in a custom container image, and deploy
the model to Vertex AI Endpoints.
C
Build a custom predictor class based on XGBoost Predictor from the Vertex AI SDK, package it and a
pickled model in a custom container image based on a Vertex built-in image, and deploy the model to Vertex AI Endpoints.
D
Build a custom predictor class based on XGBoost Predictor from the Vertex AI SDK, and package the
handler in a custom container image based on a Vertex built-in container image. Store a pickled model in Cloud Storage, and deploy the model to Vertex AI Endpoints.

PROFESSIONAL-MACHINE-LEARNING-ENGINEER Web TestEngine demo