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NEW QUESTION 43
Which of the following code blocks prints out in how many rows the expression Inc. appears in the string-type column supplier of DataFrame itemsDf?

  • A. print(itemsDf.foreach(lambda x: ‘Inc.’ in x).sum())
  • B. 1.counter = 0
    2.
    3.def count(x):
    4. if ‘Inc.’ in x[‘supplier’]:
    5. counter = counter + 1
    6.
    7.itemsDf.foreach(count)
    8.print(counter)
  • C. 1.counter = 0
    2.
    3.for index, row in itemsDf.iterrows():
    4. if ‘Inc.’ in row[‘supplier’]:
    5. counter = counter + 1
    6.
    7.print(counter)
  • D. 1.accum=sc.accumulator(0)
    2.
    3.def check_if_inc_in_supplier(row):
    4. if ‘Inc.’ in row[‘supplier’]:
    5. accum.add(1)
    6.
    7.itemsDf.foreach(check_if_inc_in_supplier)
    8.print(accum.value)
  • E. print(itemsDf.foreach(lambda x: ‘Inc.’ in x))

Answer: D

Explanation:
Explanation
Correct code block:
accum=sc.accumulator(0)
def check_if_inc_in_supplier(row):
if ‘Inc.’ in row[‘supplier’]:
accum.add(1)
itemsDf.foreach(check_if_inc_in_supplier)
print(accum.value)
To answer this question correctly, you need to know both about the DataFrame.foreach() method and accumulators.
When Spark runs the code, it executes it on the executors. The executors do not have any information about variables outside of their scope. This is whhy simply using a Python variable counter, like in the two examples that start with counter = 0, will not work. You need to tell the executors explicitly that counter is a special shared variable, an Accumulator, which is managed by the driver and can be accessed by all executors for the purpose of adding to it.
If you have used Pandas in the past, you might be familiar with the iterrows() command. Notice that there is no such command in PySpark.
The two examples that start with print do not work, since DataFrame.foreach() does not have a return value.
More info: pyspark.sql.DataFrame.foreach – PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 3

 

NEW QUESTION 44
Which of the following statements about RDDs is incorrect?

  • A. RDD stands for Resilient Distributed Dataset.
  • B. RDDs are immutable.
  • C. An RDD consists of a single partition.
  • D. RDDs are great for precisely instructing Spark on how to do a query.
  • E. The high-level DataFrame API is built on top of the low-level RDD API.

Answer: C

Explanation:
Explanation
An RDD consists of a single partition.
Quite the opposite: Spark partitions RDDs and distributes the partitions across multiple nodes.

 

NEW QUESTION 45
In which order should the code blocks shown below be run in order to create a table of all values in column attributes next to the respective values in column supplier in DataFrame itemsDf?
1. itemsDf.createOrReplaceView(“itemsDf”)
2. spark.sql(“FROM itemsDf SELECT ‘supplier’, explode(‘Attributes’)”)
3. spark.sql(“FROM itemsDf SELECT supplier, explode(attributes)”)
4. itemsDf.createOrReplaceTempView(“itemsDf”)

  • A. 1, 3
  • B. 0
  • C. 4, 3
  • D. 1, 2
  • E. 4, 2

Answer: C

Explanation:
Explanation
Static notebook | Dynamic notebook: See test 1

 

NEW QUESTION 46
The code block displayed below contains an error. The code block should configure Spark so that DataFrames up to a size of 20 MB will be broadcast to all worker nodes when performing a join.
Find the error.
Code block:

  • A. The command is evaluated lazily and needs to be followed by an action.
  • B. Spark will only broadcast DataFrames that are much smaller than the default value.
  • C. The passed limit has the wrong variable type.
  • D. Spark will only apply the limit to threshold joins and not to other joins.
  • E. The correct option to write configurations is through spark.config and not spark.conf.
  • F. spark.conf.set(“spark.sql.autoBroadcastJoinThreshold”, 20)

Answer: B

Explanation:
Explanation
This is question is hard. Let’s assess the different answers one-by-one.
Spark will only broadcast DataFrames that are much smaller than the default value.
This is correct. The default value is 10 MB (10485760 bytes). Since the configuration for spark.sql.autoBroadcastJoinThreshold expects a number in bytes (and not megabytes), the code block sets the limits to merely 20 bytes, instead of the requested 20 * 1024 * 1024 (= 20971520) bytes.
The command is evaluated lazily and needs to be followed by an action.
No, this command is evaluated right away!
Spark will only apply the limit to threshold joins and not to other joins.
There are no “threshold joins”, so this option does not make any sense.
The correct option to write configurations is through spark.config and not spark.conf.
No, it is indeed spark.conf!
The passed limit has the wrong variable type.
The configuration expects the number of bytes, a number, as an input. So, the 20 provided in the code block is fine.

 

NEW QUESTION 47
……