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NEW QUESTION 50
The code block shown below should return a new 2-column DataFrame that shows one attribute from column attributes per row next to the associated itemName, for all suppliers in column supplier whose name includes Sports. Choose the answer that correctly fills the blanks in the code block to accomplish this.
Sample of DataFrame itemsDf:
1.+------+----------------------------------+-----------------------------+-------------------+
2.|itemId|itemName |attributes |supplier |
3.+------+----------------------------------+-----------------------------+-------------------+
4.|1 |Thick Coat for Walking in the Snow|[blue, winter, cozy] |Sports Company Inc.|
5.|2 |Elegant Outdoors Summer Dress |[red, summer, fresh, cooling]|YetiX |
6.|3 |Outdoors Backpack |[green, summer, travel] |Sports Company Inc.|
7.+------+----------------------------------+-----------------------------+-------------------+ Code block:
itemsDf.__1__(__2__).select(__3__, __4__)
2. col("supplier").contains("Sports")
3. "itemName"
4. "attributes"B. 1. filter
2. col("supplier").contains("Sports")
3. "itemName"
4. explode("attributes")C. 1. where
2. col(supplier).contains("Sports")
3. explode(attributes)
4. itemNameD. 1. where
2. "Sports".isin(col("Supplier"))
3. "itemName"
4. array_explode("attributes")E. 1. filter
2. col("supplier").isin("Sports")
3. "itemName"
4. explode(col("attributes"))
Answer: B
Explanation:
Explanation
Output of correct code block:
+----------------------------------+------+
|itemName |col |
+----------------------------------+------+
|Thick Coat for Walking in the Snow|blue |
|Thick Coat for Walking in the Snow|winter|
|Thick Coat for Walking in the Snow|cozy |
|Outdoors Backpack |green |
|Outdoors Backpack |summer|
|Outdoors Backpack |travel|
+----------------------------------+------+
The key to solving this question is knowing about Spark's explode operator. Using this operator, you can extract values from arrays into single rows. The following guidance steps through the answers systematically from the first to the last gap. Note that there are many ways to solving the gap questions and filtering out wrong answers, you do not always have to start filtering out from the first gap, but can also exclude some answers based on obvious problems you see with them.
The answers to the first gap present you with two options: filter and where. These two are actually synonyms in PySpark, so using either of those is fine. The answer options to this gap therefore do not help us in selecting the right answer.
The second gap is more interesting. One answer option includes "Sports".isin(col("Supplier")). This construct does not work, since Python's string does not have an isin method. Another option contains col(supplier). Here, Python will try to interpret supplier as a variable. We have not set this variable, so this is not a viable answer. Then, you are left with answers options that include col ("supplier").contains("Sports") and col("supplier").isin("Sports"). The question states that we are looking for suppliers whose name includes Sports, so we have to go for the contains operator here.
We would use the isin operator if we wanted to filter out for supplier names that match any entries in a list of supplier names.
Finally, we are left with two answers that fill the third gap both with "itemName" and the fourth gap either with explode("attributes") or "attributes". While both are correct Spark syntax, only explode ("attributes") will help us achieve our goal. Specifically, the question asks for one attribute from column attributes per row - this is what the explode() operator does.
One answer option also includes array_explode() which is not a valid operator in PySpark.
More info: pyspark.sql.functions.explode - PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 3
NEW QUESTION 51
Which of the following code blocks performs an inner join between DataFrame itemsDf and DataFrame transactionsDf, using columns itemId and transactionId as join keys, respectively?
Answer: C
Explanation:
Explanation
More info: pyspark.sql.DataFrame.join - PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 2
NEW QUESTION 52
Which of the following describes the role of the cluster manager?
Answer: B
Explanation:
Explanation
The cluster manager allocates resources to Spark applications and maintains the executor processes in client mode.
Correct. In cluster mode, the cluster manager is located on a node other than the client machine. From there it starts and ends executor processes on the cluster nodes as required by the Spark application running on the Spark driver.
The cluster manager allocates resources to Spark applications and maintains the executor processes in remote mode.
Wrong, there is no "remote" execution mode in Spark. Available execution modes are local, client, and cluster.
The cluster manager allocates resources to the DataFrame manager
Wrong, there is no "DataFrame manager" in Spark.
The cluster manager schedules tasks on the cluster in client mode.
No, in client mode, the Spark driver schedules tasks on the cluster - not the cluster manager.
The cluster manager schedules tasks on the cluster in local mode.
Wrong: In local mode, there is no "cluster". The Spark application is running on a single machine, not on a cluster of machines.
NEW QUESTION 53
Which of the following code blocks returns a new DataFrame in which column attributes of DataFrame itemsDf is renamed to feature0 and column supplier to feature1?
2.itemsDf.withColumnRenamed("supplier", "feature1")C. itemsDf.withColumnRenamed("attributes", "feature0").withColumnRenamed("supplier", "feature1")D. itemsDf.withColumnRenamed(attributes, feature0).withColumnRenamed(supplier, feature1)E. itemsDf.withColumnRenamed(col("attributes"), col("feature0"), col("supplier"), col("feature1"))
Answer: C
Explanation:
Explanation
itemsDf.withColumnRenamed("attributes", "feature0").withColumnRenamed("supplier", "feature1") Correct! Spark's DataFrame.withColumnRenamed syntax makes it relatively easy to change the name of a column.
itemsDf.withColumnRenamed(attributes, feature0).withColumnRenamed(supplier, feature1) Incorrect. In this code block, the Python interpreter will try to use attributes and the other column names as variables. Needless to say, they are undefined, and as a result the block will not run.
itemsDf.withColumnRenamed(col("attributes"), col("feature0"), col("supplier"), col("feature1")) Wrong. The DataFrame.withColumnRenamed() operator takes exactly two string arguments. So, in this answer both using col() and using four arguments is wrong.
itemsDf.withColumnRenamed("attributes", "feature0")
itemsDf.withColumnRenamed("supplier", "feature1")
No. In this answer, the returned DataFrame will only have column supplier be renamed, since the result of the first line is not written back to itemsDf.
itemsDf.withColumn("attributes", "feature0").withColumn("supplier", "feature1") Incorrect. While withColumn works for adding and naming new columns, you cannot use it to rename existing columns.
More info: pyspark.sql.DataFrame.withColumnRenamed - PySpark 3.1.2 documentation Static notebook | Dynamic notebook: See test 3
NEW QUESTION 54
Which of the following DataFrame methods is classified as a transformation?
Answer: A
Explanation:
Explanation
DataFrame.select()
Correct, DataFrame.select() is a transformation. When the command is executed, it is evaluated lazily and returns an RDD when it is triggered by an action.
DataFrame.foreach()
Incorrect, DataFrame.foreach() is not a transformation, but an action. The intention of foreach() is to apply code to each element of a DataFrame to update accumulator variables or write the elements to external storage. The process does not return an RDD - it is an action!
DataFrame.first()
Wrong. As an action, DataFrame.first() executed immediately and returns the first row of a DataFrame.
DataFrame.count()
Incorrect. DataFrame.count() is an action and returns the number of rows in a DataFrame.
DataFrame.show()
No, DataFrame.show() is an action and displays the DataFrame upon execution of the command.
NEW QUESTION 55
......
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