Exam Certified Associate Developer for Apache Spark topic 1 question 36 discussion

doesn't reverse sorted alphabetically mean descending????

C is the right answer. See code below with Spark 3.5.1 # Sort a DataFrame by a column in reverse alphabetical order from pyspark.sql import SparkSession from pyspark.sql.functions import col, asc, desc from pyspark.errors import PySparkTypeError 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"]) storesDF.orderBy("division", ascending = False).show() storesDF.orderBy(["division"], ascending = [0]).show() storesDF.orderBy(col("division").asc()).show() storesDF.sort("division", ascending = False).show() storesDF.sort(desc("division")).show()

C is the correct answer

C is the right answer because it returns the dataframe in ascending order.

b i tesetd ut

C. It is the only option "not returning" the dataframe in descending(reverse) order. All other formats are returning the descending order. In Oprtion E, if we import the desc function,. it will not throw error and will return the dataframe in descending order.

E. storesDF.sort(desc("division")) Option E correctly uses the desc function to specify the descending order for sorting. Thank you for providing additional information and clarification.

Option A and D is giving errors. ~ cannot be used in ascending. Right way is to use ascending=False. Most relevant option is C which is sorting the data in ascending order , Option A, D have some typos it should be = instead of ~.

C is the answer. Only C will make the data in ascending order. Tested the code.

E is right

It's C: storesDF.orderBy(col("division").asc()) => storesDF.orderBy(col("division").desc())

Option E is right answer