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NEW QUESTION 38 The code block displayed below contains an error. The code block should arrange the rows of DataFrame transactionsDf using information from two columns in an ordered fashion, arranging first by column value, showing smaller numbers at the top and greater numbers at the bottom, and then by column predError, for which all values should be arranged in the inverse way of the order of items in column value. Find the error. Code block: transactionsDf.orderBy('value', ascnullsfirst(col('predError')))

• A. Column predError should be sorted by descnullsfirst() instead.
• B. Column value should be wrapped by the col() operator.
• C. Instead of orderBy, sort should be used.
• D. Column predError should be sorted in a descending way, putting nulls last.
• E. Two orderBy statements with calls to the individual columns should be chained, instead of having both columns in one orderBy statement.

Answer: D Explanation: Explanation Correct code block: transactionsDf.orderBy('value', descnullslast('predError')) Column predError should be sorted in a descending way, putting nulls last. Correct! By default, Spark sorts ascending, putting nulls first. So, the inverse sort of the default sort is indeed descnullslast. Instead of orderBy, sort should be used. No. DataFrame.sort() orders data per partition, it does not guarantee a global order. This is why orderBy is the more appropriate operator here. Column value should be wrapped by the col() operator. Incorrect. DataFrame.sort() accepts both string and Column objects. Column predError should be sorted by descnullsfirst() instead. Wrong. Since Spark's default sort order matches ascnullsfirst(), nulls would have to come last when inverted. Two orderBy statements with calls to the individual columns should be chained, instead of having both columns in one orderBy statement. No, this would just sort the DataFrame by the very last column, but would not take information from both columns into account, as noted in the question. More info: pyspark.sql.DataFrame.orderBy - PySpark 3.1.2 documentation, pyspark.sql.functions.descnullslast - PySpark 3.1.2 documentation, sort() vs orderBy() in Spark | Towards Data Science Static notebook | Dynamic notebook: See test 3   NEW QUESTION 39 The code block displayed below contains an error. The code block should configure Spark to split data in 20 parts when exchanging data between executors for joins or aggregations. Find the error. Code block: spark.conf.set(spark.sql.shuffle.partitions, 20)

• A. The code block sets the wrong option.
• B. The code block uses the wrong command for setting an option.
• C. The code block is missing a parameter.
• D. The code block sets the incorrect number of parts.
• E. The code block expresses the option incorrectly.

Answer: E Explanation: Explanation Correct code block: spark.conf.set("spark.sql.shuffle.partitions", 20) The code block expresses the option incorrectly. Correct! The option should be expressed as a string. The code block sets the wrong option. No, spark.sql.shuffle.partitions is the correct option for the use case in the question. The code block sets the incorrect number of parts. Wrong, the code block correctly states 20 parts. The code block uses the wrong command for setting an option. No, in PySpark spark.conf.set() is the correct command for setting an option. The code block is missing a parameter. Incorrect, spark.conf.set() takes two parameters. More info: Configuration - Spark 3.1.2 Documentation   NEW QUESTION 40 The code block displayed below contains an error. The code block should merge the rows of DataFrames transactionsDfMonday and transactionsDfTuesday into a new DataFrame, matching column names and inserting null values where column names do not appear in both DataFrames. Find the error. Sample of DataFrame transactionsDfMonday: 1.+-------------+---------+-----+-------+---------+----+ 2.|transactionId|predError|value|storeId|productId| f| 3.+-------------+---------+-----+-------+---------+----+ 4.| 5| null| null| null| 2|null| 5.| 6| 3| 2| 25| 2|null| 6.+-------------+---------+-----+-------+---------+----+ Sample of DataFrame transactionsDfTuesday: 1.+-------+-------------+---------+-----+ 2.|storeId|transactionId|productId|value| 3.+-------+-------------+---------+-----+ 4.| 25| 1| 1| 4| 5.| 2| 2| 2| 7| 6.| 3| 4| 2| null| 7.| null| 5| 2| null| 8.+-------+-------------+---------+-----+ Code block: sc.union([transactionsDfMonday, transactionsDfTuesday])

• A. Instead of the Spark context, transactionDfMonday should be called with the unionByName method instead of the union method, making sure to not use its default arguments.
• B. Instead of union, the concat method should be used, making sure to not use its default arguments.
• C. The DataFrames' RDDs need to be passed into the sc.union method instead of the DataFrame variable names.
• D. Instead of the Spark context, transactionDfMonday should be called with the union method.
• E. Instead of the Spark context, transactionDfMonday should be called with the join method instead of the union method, making sure to use its default arguments.

Answer: A Explanation: Explanation Correct code block: transactionsDfMonday.unionByName(transactionsDfTuesday, True) Output of correct code block: +-------------+---------+-----+-------+---------+----+ |transactionId|predError|value|storeId|productId| f| +-------------+---------+-----+-------+---------+----+ | 5| null| null| null| 2|null| | 6| 3| 2| 25| 2|null| | 1| null| 4| 25| 1|null| | 2| null| 7| 2| 2|null| | 4| null| null| 3| 2|null| | 5| null| null| null| 2|null| +-------------+---------+-----+-------+---------+----+ For solving this question, you should be aware of the difference between the DataFrame.union() and DataFrame.unionByName() methods. The first one matches columns independent of their names, just by their order. The second one matches columns by their name (which is asked for in the question). It also has a useful optional argument, allowMissingColumns. This allows you to merge DataFrames that have different columns - just like in this example. sc stands for SparkContext and is automatically provided when executing code on Databricks. While sc.union() allows you to join RDDs, it is not the right choice for joining DataFrames. A hint away from sc.union() is given where the question talks about joining "into a new DataFrame". concat is a method in pyspark.sql.functions. It is great for consolidating values from different columns, but has no place when trying to join rows of multiple DataFrames. Finally, the join method is a contender here. However, the default join defined for that method is an inner join which does not get us closer to the goal to match the two DataFrames as instructed, especially given that with the default arguments we cannot define a join condition. More info: - pyspark.sql.DataFrame.unionByName - PySpark 3.1.2 documentation - pyspark.SparkContext.union - PySpark 3.1.2 documentation - pyspark.sql.functions.concat - PySpark 3.1.2 documentation Static notebook | Dynamic notebook: See test 3   NEW QUESTION 41 Which of the following code blocks creates a new DataFrame with two columns season and windspeedms where column season is of data type string and column windspeedms is of data type double?

• A. spark.DataFrame({"season": ["winter","summer"], "windspeedms": [4.5, 7.5]})
• B. 1. from pyspark.sql import types as T
• spark.createDataFrame((("summer", 4.5), ("winter", 7.5)), T.StructType([T.StructField("season",
• C. spark.createDataFrame([("summer", 4.5), ("winter", 7.5)], ["season", "windspeedms"])
• D. spark.createDataFrame({"season": ["winter","summer"], "windspeedms": [4.5, 7.5]})
• E. spark.newDataFrame([("summer", 4.5), ("winter", 7.5)], ["season", "windspeedms"])
• F. CharType()), T.StructField("season", T.DoubleType())]))

Answer: C Explanation: Explanation spark.createDataFrame([("summer", 4.5), ("winter", 7.5)], ["season", "windspeedms"]) Correct. This command uses the Spark Session's createDataFrame method to create a new DataFrame. Notice how rows, columns, and column names are passed in here: The rows are specified as a Python list. Every entry in the list is a new row. Columns are specified as Python tuples (for example ("summer", 4.5)). Every column is one entry in the tuple. The column names are specified as the second argument to createDataFrame(). The documentation (link below) shows that "when schema is a list of column names, the type of each column will be inferred from data" (the first argument). Since values 4.5 and 7.5 are both float variables, Spark will correctly infer the double type for column windspeedms. Given that all values in column "season" contain only strings, Spark will cast the column appropriately as string. Find out more about SparkSession.createDataFrame() via the link below. spark.newDataFrame([("summer", 4.5), ("winter", 7.5)], ["season", "windspeedms"]) No, the SparkSession does not have a newDataFrame method. from pyspark.sql import types as T spark.createDataFrame((("summer", 4.5), ("winter", 7.5)), T.StructType([T.StructField("season", T.CharType()), T.StructField("season", T.DoubleType())])) No. pyspark.sql.types does not have a CharType type. See link below for available data types in Spark. spark.createDataFrame({"season": ["winter","summer"], "windspeedms": [4.5, 7.5]}) No, this is not correct Spark syntax. If you have considered this option to be correct, you may have some experience with Python's pandas package, in which this would be correct syntax. To create a Spark DataFrame from a Pandas DataFrame, you can simply use spark.createDataFrame(pandasDf) where pandasDf is the Pandas DataFrame. Find out more about Spark syntax options using the examples in the documentation for SparkSession.createDataFrame linked below. spark.DataFrame({"season": ["winter","summer"], "windspeedms": [4.5, 7.5]}) No, the Spark Session (indicated by spark in the code above) does not have a DataFrame method. More info: pyspark.sql.SparkSession.createDataFrame - PySpark 3.1.1 documentation and Data Types - Spark 3.1.2 Documentation Static notebook | Dynamic notebook: See test 1   NEW QUESTION 42 Which of the following code blocks can be used to save DataFrame transactionsDf to memory only, recalculating partitions that do not fit in memory when they are needed?

• A. transactionsDf.storagelevel('MEMORYONLY')
• B. from pyspark import StorageLevel transactionsDf.persist(StorageLevel.MEMORY_ONLY)
• C. from pyspark import StorageLevel transactionsDf.cache(StorageLevel.MEMORY_ONLY)
• D. transactionsDf.clear_persist()
• E. transactionsDf.persist()
• F. transactionsDf.cache()

Answer: B Explanation: Explanation from pyspark import StorageLevel transactionsDf.persist(StorageLevel.MEMORYONLY) Correct. Note that the storage level MEMORYONLY means that all partitions that do not fit into memory will be recomputed when they are needed. transactionsDf.cache() This is wrong because the default storage level of DataFrame.cache() is MEMORYANDDISK, meaning that partitions that do not fit into memory are stored on disk. transactionsDf.persist() This is wrong because the default storage level of DataFrame.persist() is MEMORYANDDISK. transactionsDf.clearpersist() Incorrect, since clearpersist() is not a method of DataFrame. transactionsDf.storagelevel('MEMORYONLY') Wrong. storage_level is not a method of DataFrame. More info: RDD Programming Guide - Spark 3.0.0 Documentation, pyspark.sql.DataFrame.persist - PySpark 3.0.0 documentation (https://bit.ly/3sxHLVC , https://bit.ly/3j2N6B9)   NEW QUESTION 43 ......