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NEW QUESTION 38
Which of the following code blocks displays the 10 rows with the smallest values of column value in DataFrame transactionsDf in a nicely formatted way?
Answer: D
Explanation:
Explanation
show() is the correct method to look for here, since the question specifically asks for displaying the rows in a nicely formatted way. Here is the output of show (only a few rows shown):
+-------------+---------+-----+-------+---------+----+---------------+
|transactionId|predError|value|storeId|productId| f|transactionDate|
+-------------+---------+-----+-------+---------+----+---------------+
| 3| 3| 1| 25| 3|null| 1585824821|
| 5| null| 2| null| 2|null| 1575285427|
| 4| null| 3| 3| 2|null| 1583244275|
+-------------+---------+-----+-------+---------+----+---------------+
With regards to the sorting, specifically in ascending order since the smallest values should be shown first, the following expressions are valid:
- transactionsDf.sort(col("value")) ("ascending" is the default sort direction in the sort method)
- transactionsDf.sort(asc(col("value")))
- transactionsDf.sort(asc("value"))
- transactionsDf.sort(transactionsDf.value.asc())
- transactionsDf.sort(transactionsDf.value)
Also, orderBy is just an alias of sort, so all of these expressions work equally well using orderBy.
Static notebook | Dynamic notebook: See test 1
NEW QUESTION 39
The code block shown below should return a two-column DataFrame with columns transactionId and supplier, with combined information from DataFrames itemsDf and transactionsDf. The code block should merge rows in which column productId of DataFrame transactionsDf matches the value of column itemId in DataFrame itemsDf, but only where column storeId of DataFrame transactionsDf does not match column itemId of DataFrame itemsDf. Choose the answer that correctly fills the blanks in the code block to accomplish this.
Code block:
transactionsDf.__1__(itemsDf, __2__).__3__(__4__)
2. transactionsDf.productId==itemsDf.itemId, how="inner"
3. select
4. "transactionId", "supplier"B. 1. join
2. [transactionsDf.productId==itemsDf.itemId, transactionsDf.storeId!=itemsDf.itemId]
3. select
4. "transactionId", "supplier"C. 1. join
2. transactionsDf.productId==itemsDf.itemId, transactionsDf.storeId!=itemsDf.itemId
3. filter
4. "transactionId", "supplier"D. 1. select
2. "transactionId", "supplier"
3. join
4. [transactionsDf.storeId!=itemsDf.itemId, transactionsDf.productId==itemsDf.itemId]E. 1. filter
2. "transactionId", "supplier"
3. join
4. "transactionsDf.storeId!=itemsDf.itemId, transactionsDf.productId==itemsDf.itemId"
Answer: B
Explanation:
Explanation
This question is pretty complex and, in its complexity, is probably above what you would encounter in the exam. However, reading the question carefully, you can use your logic skills to weed out the wrong answers here.
First, you should examine the join statement which is common to all answers. The first argument of the join() operator (documentation linked below) is the DataFrame to be joined with. Where join is in gap 3, the first argument of gap 4 should therefore be another DataFrame. For none of the questions where join is in the third gap, this is the case. So you can immediately discard two answers.
For all other answers, join is in gap 1, followed by .(itemsDf, according to the code block. Given how the join() operator is called, there are now three remaining candidates.
Looking further at the join() statement, the second argument (on=) expects "a string for the join column name, a list of column names, a join expression (Column), or a list of Columns", according to the documentation. As one answer option includes a list of join expressions (transactionsDf.productId==itemsDf.itemId, transactionsDf.storeId!=itemsDf.itemId) which is unsupported according to the documentation, we can discard that answer, leaving us with two remaining candidates.
Both candidates have valid syntax, but only one of them fulfills the condition in the question "only where column storeId of DataFrame transactionsDf does not match column itemId of DataFrame itemsDf". So, this one remaining answer option has to be the correct one!
As you can see, although sometimes overwhelming at first, even more complex questions can be figured out by rigorously applying the knowledge you can gain from the documentation during the exam.
More info: pyspark.sql.DataFrame.join - PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 3
NEW QUESTION 40
Which of the following is the deepest level in Spark's execution hierarchy?
Answer: E
Explanation:
Explanation
The hierarchy is, from top to bottom: Job, Stage, Task.
Executors and slots facilitate the execution of tasks, but they are not directly part of the hierarchy. Executors are launched by the driver on worker nodes for the purpose of running a specific Spark application. Slots help Spark parallelize work. An executor can have multiple slots which enable it to process multiple tasks in parallel.
NEW QUESTION 41
Which of the elements in the labeled panels represent the operation performed for broadcast variables?
Larger image
Answer: B
Explanation:
Explanation
2,3
Correct! Both panels 2 and 3 represent the operation performed for broadcast variables. While a broadcast operation may look like panel 3, with the driver being the bottleneck, it most probably looks like panel 2.
This is because the torrent protocol sits behind Spark's broadcast implementation. In the torrent protocol, each executor will try to fetch missing broadcast variables from the driver or other nodes, preventing the driver from being the bottleneck.
1,2
Wrong. While panel 2 may represent broadcasting, panel 1 shows bi-directional communication which does not occur in broadcast operations.
3
No. While broadcasting may materialize like shown in panel 3, its use of the torrent protocol also enables communciation as shown in panel 2 (see first explanation).
1,3,4
No. While panel 2 shows broadcasting, panel 1 shows bi-directional communication - not a characteristic of broadcasting. Panel 4 shows uni-directional communication, but in the wrong direction.
Panel 4 resembles more an accumulator variable than a broadcast variable.
2,5
Incorrect. While panel 2 shows broadcasting, panel 5 includes bi-directional communication - not a characteristic of broadcasting.
More info: Broadcast Join with Spark - henning.kropponline.de
NEW QUESTION 42
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