Exam Certified Associate Developer for Apache Spark topic 1 question 42 discussion

The right answer is D. pyspark.sql.functions.udf(f=None, returnType=StringType) The default return type is string, but this question requires integer returning. so it should be D. "The return type of the assessPerformanceUDF() is not specified in the udf() operation."

D is the right answer as otherwise the return type is the default StringType(). Test code below: from pyspark.sql import SparkSession from pyspark.sql.functions import col, udf from pyspark.sql.types import IntegerType spark = SparkSession.builder.appName("MyApp").getOrCreate() data = [ (0, 3, "A"), (1, 1, "A"), (2, 2, "A"), ] storesDF = spark.createDataFrame(data, ["storeID", "customerSatisfaction", "division"]) def assessPerformance(x): return 1 if x > 3 else 0 print("IntegerType()") assessPerformanceUDF = udf(assessPerformance, IntegerType()) df = storesDF.withColumn("result", assessPerformanceUDF(col("customerSatisfaction"))) df.printSchema() print("Default") assessPerformanceUDF = udf(assessPerformance) df = storesDF.withColumn("result", assessPerformanceUDF(col("customerSatisfaction"))) df.printSchema()

correct answer is D

It is necessary to inform the return type as IntegerType(). from pyspark.sql.functions import udf, col from pyspark.sql.types import IntegerType storesDF = spark.createDataFrame([('1', '123'), ('2', '234')], ['id', 'customerSatisfaction']) assessPerformance = lambda x: int(x) assessPerformanceUDF = udf(assessPerformance, IntegerType()) storesDF.withColumn('result', assessPerformanceUDF(col('customerSatisfaction'))).printSchema()

| 1. When `f` is a Python function: | | `returnType` defaults to string type and can be optionally specified. The produced | object must match the specified type. In this case, this API works as if | `register(name, f, returnType=StringType())`.

The error in the code block is that the return type of the assessPerformanceUDF() is not specified in the udf() operation. In PySpark, when you register a Python function as a UDF, you should also specify the return type. This is important because Spark SQL needs to understand the return type to properly handle the UDF. Therefore, the correct answer is:

if they mean that - is =, then we need a second parameter, the output type. so, D is the answe

Right answer is D, return type has to be specified into udf() or it will return StringType by default, the code should be : function_UDF = udf(function, returnType=IntegerType())

The error in the code block is A. The function assessPerformance() needs to be passed as a parameter to the udf() operation in order to create a UDF from it. The correct code block should be: assessPerformanceUDF = udf(assessPerformance) storesDF.withColumn("result", assessPerformanceUDF(col(