Exam Certified Associate Developer for Apache Spark topic 1 question 39 discussion

Option D is correct. See example code below: from pyspark.sql import SparkSession spark = SparkSession.builder.appName("MyApp").getOrCreate() data = [ (0, 43161, "A"), (1, 51200, "A"), (2, None, "B"), (3, 78367, "B"), (4, None, "C"), ] storesDF = spark.createDataFrame(data, ["storeID", "sqft", "division"]) def myFunction(row): return row[0] [myFunction(row) for row in storesDF.collect()]

There are many way to apply a function to dataframe. 1. apply, as shown in option D. but it should be apply(assessPerformance) 2. list comprehension: for row in df.collect() 3. foreach 4. map, but for RDD majorly

The correct answer is D. Explanation: Option A uses the take() method to extract three rows from the DataFrame, but it applies the assessPerformance() function to each row outside of the DataFrame context. Option B attempts to apply the assessPerformance() function to each row, but it doesn't reference the row object in any way. Option C tries to apply the assessPerformance() function to the entire DataFrame but does so using an incorrect syntax. Option D correctly applies the assessPerformance() function to each row of the DataFrame using a list comprehension over the result of the collect() method. Option E is similar to D, but it will iterate over rows individually instead of using the collect() method to retrieve all rows at once. While this is still a valid approach, it may be less efficient.