PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #237
PROFESSIONAL-MACHINE-LEARNING-ENGINEER Question #237: Real Exam Question with Answer & Explanation
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Question
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?
Options
- AStore a pickled model in Cloud Storage. Build a Flask-based app, package the app in a custom
- BBuild a Flask-based app, package the app and a pickled model in a custom container image, and
- CBuild a custom predictor class based on XGBoost Predictor from the Vertex AI SDK, package it
- DBuild a custom predictor class based on XGBoost Predictor from the Vertex AI SDK, and package
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