In a machine learning project, you have a trained model and a new batch of data to make predictions. Explain how to score the model using features from a feature store table in Databricks, including the necessary code snippets.

Simulated

To score a model using features from a feature store table in Databricks, first, retrieve the required features from the feature store table using the feature store API. Then, transform the new data to match the schema of the features in the feature store table. Finally, use the trained model to make predictions on the transformed data.

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Scoring a model using features from a feature store table in Databricks is not possible as the feature store API does not support this functionality.

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To score a model, use the same features that were used during the training of the model, regardless of whether they are stored in a feature store table or not.

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Scoring a model using features from a feature store table in Databricks requires manual feature engineering and transformation of the new data to match the model's input requirements.

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In a machine learning project, you have a trained model and a new batch of data to make predictions. Explain how to score the model using features from a feature store table in Databricks, including the necessary code snippets.