Free Databricks Machine Learning Associate Exam Dumps

The OneHotEncoder in Spark ML requires numerical indices as inputs rather than string labels. Therefore, you need to first convert the string columns to numerical indices using StringIndexer. After that, you can apply OneHotEncoder to these indices.

Corrected code:

from pyspark.ml.feature import StringIndexer, OneHotEncoder # Convert string column to index indexers = [StringIndexer(inputCol=col, outputCol=col+'_index') for col in input_columns] indexer_model = Pipeline(stages=indexers).fit(features_df) indexed_features_df = indexer_model.transform(features_df) # One-hot encode the indexed columns ohe = OneHotEncoder(inputCols=[col+'_index' for col in input_columns], outputCols=output_columns) ohe_model = ohe.fit(indexed_features_df) ohe_features_df = ohe_model.transform(indexed_features_df)

PySpark ML Documentation

To apply the Pandas UDF predict to each record of a Spark DataFrame, you use the mapInPandas method. This method allows the Pandas UDF to operate on partitions of the DataFrame as pandas DataFrames, applying the specified function (predict in this case) to each partition. The correct code completion to execute this is simply mapInPandas(predict), which specifies the UDF to use without additional arguments or incorrect function calls. Reference:

PySpark DataFrame documentation (Using mapInPandas with UDFs).