Databricks-Machine-Learning-Professional Exam Dumps | A Machine Learning Engineer needs to build a time series model. In Databricks, they have created isolated

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Question 8/96

A Machine Learning Engineer needs to build a time series model. In Databricks, they have created isolated environments in different workspaces for development, staging, and production.
To manage this model, they are planning on using a "deploy code" strategy. They are concerned that the model trained in development will not remain consistent across environments, due to differences in the dependencies installed. What can they do to ensure that the model is trained with the same packages?

A. Use the same Databricks Runtime (DBR) version across environments and define dependencies in a lock file that is installed as part of the job.

B. Use custom init scripts that install the necessary packages as part of the job.

C. Use MLflow to track the model parameters and metrics in development, then export the same training conditions in staging and production.

D. Use Databricks Repos to store the model code and install the required libraries in the notebook.

Question 8/96

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