Using a feature store to score a model with features from the feature store table in Databricks provides several benefits, such as maintaining consistency, reducing data duplication, and improving the efficiency of the scoring process. To achieve this, 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.

Using a feature store to score a model is not necessary as it can be done directly from the dataset without using a feature store.

Scoring a model using features from a feature store table in Databricks is a manual process that requires custom code for each step.

Using a feature store to score a model with features from the feature store table in Databricks does not provide any significant benefits and can be avoided.