Exam Certified Associate Developer for Apache Spark topic 1 question 26 discussion

Both option A and E work with spark 3.5.1. But A is better for backward compatibility. See code example below: from pyspark.sql import SparkSession from pyspark.sql.functions import col, explode spark = SparkSession.builder.appName("MyApp").getOrCreate() data = [ (0, ["value 1", "value 2", "value 3"]), (1, ["value 1", "value 2", "value 3"]), (2, ["value 1", "value 2", "value 3"]), ] storesDF = spark.createDataFrame(data, ["storeID", "productCategories"]) storesDF.withColumn("productCategories", explode(col("productCategories"))).show() # A. storesDF.withColumn("productCategories", explode("productCategories")).show() # E.

A and E are correct

Both A and E are correct according to the new version

A is correct, use below code to test: from pyspark.sql import SparkSession # Initializing Spark session spark = SparkSession.builder.appName("test").getOrCreate() # 1. Creating DataFrame with an array column data_array = [ (1, ["electronics", "clothes", "toys"]), (2, ["groceries", "electronics"]), (3, ["books", "clothes"]), ] storesDF = spark.createDataFrame(data_array, ["ID", "productCategories"]) storesDF.show() df_array = storesDF.withColumn("productCategories", explode(col("productCategories"))) df_array.show()

Both A and E are correct.

While the Explode function allows for a str or Column input, this requires the col() wrapper because it is used in a withColumn() call, where the 2nd parameter requires the column object. https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrame.withColumn.html?highlight=withcolumn#pyspark.sql.DataFrame.withColumn

Option A is correct: storesDF.withColumn("productCategories", explode(col("productCategories"))). Explanation: The explode function is used to transform a column of arrays or maps into multiple rows, one for each element in the array or map. In this case, productCategories is a column with arrays of strings. The withColumn function is used to add a new column or update an existing column. The first argument is the name of the new or existing column, and the second argument is the expression that defines the values for the column.