Free DATABRICKS-MACHINE-LEARNING-ASSOCIATE Web TestEngine Demo

Q10 Single choice

The data science team at a retailer has developed a custom MLflow pyfunc model with private Python dependencies. They now want to deploy and serve the model for real-time inference using Databricks Model Serving. However, they are getting dependency errors during deployment.

Which is the correct approach to resolve the issue?

A
Add the dependencies to the MLflow model using the add_libraries_to_model method
B
Log the dependencies separately using the log_dependencies method
C
Log the dependencies together with the model using log_model method with pip_requirements or extra_pip_requirements parameter
D
Use an MLflow native flavor model instead of the custom pyfunc model

DATABRICKS-MACHINE-LEARNING-ASSOCIATE Web TestEngine demo