Databricks Databricks-Machine-Learning-Associate Actual Questions

To apply the Pandas UDFpredictto each record of a Spark DataFrame, you use themapInPandasmethod. This method allows the Pandas UDF to operate on partitions of the DataFrame as pandas DataFrames, applying the specified function (predictin this case) to each partition. The correct code completion to execute this is simplymapInPandas(predict), which specifies the UDF to use without additional arguments orincorrect function calls.References:

• PySpark DataFrame documentation (Using mapInPandas with UDFs).

To register a model that has been identified by a specific run_id in the MLflow Model Registry, the appropriate line of code is:

mlflow.register_model(f"runs:/{run_id}/model","best_model")

This code correctly specifies the path to the model within the run (runs:/{run_id}/model) and registers it under the name "best_model" in the Model Registry. This allows the model to be tracked, managed, and transitioned through different stages (e.g., Staging, Production) within the MLflow ecosystem.

References

• MLflow documentation on model registry: https://www.mlflow.org/docs/latest/model-registry.html#registering-a-model

The Root Mean Squared Error (RMSE) is a standard and widely used metric for evaluating the accuracy of regression models. The statement that it is invalid is incorrect. Here’s a breakdown of why the other statements are or are not valid:

• Transformations and RMSE Calculation:If the model predictions were transformed (e.g., using log), they should be converted back to their original scale before calculating RMSE to ensure accuracy in the evaluation. Missteps in this conversion process can lead to misleading RMSE values.
• Accuracy of Models:Without additional information, we can't definitively say which model is more accurate without considering their RMSE values properly scaled back to the original price scale.
• Appropriateness of RMSE:RMSE is entirely valid for regression problems as it provides a measure of how accurately a model predicts the outcome, expressed in the same units as the dependent variable.

References

• "Applied Predictive Modeling" by Max Kuhn and Kjell Johnson (Springer, 2013), particularly the chapters discussing model evaluation metrics.

To parallelize the inference of group-specific models using the Pandas Function API in PySpark, you can use theapplyInPandasfunction. This function allows you to apply a Python function on each group of a DataFrame and return a DataFrame, leveraging the power of pandas UDFs (user-defined functions) for better performance.

prediction_df = ( df.groupby("device_id") .applyInPandas(apply_model, schema=apply_return_schema) )

In this code:

• groupby("device_id"): Groups the DataFrame by the "device_id" column.
• applyInPandas(apply_model, schema=apply_return_schema): Applies theapply_modelfunction to each group and specifies the schema of the return DataFrame.

References:

• PySpark Pandas UDFs Documentation