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Question 1

Which of the following code blocks efficiently converts DataFrame transactionsDf from 12 into 24 partitions?

Options :

A : transactionsDf.repartition(24, boost=True)

B : transactionsDf.repartition()

C : transactionsDf.repartition("itemId", 24)

D : transactionsDf.coalesce(24)

E : transactionsDf.repartition(24)

Answer: A

Question 2

Which of the following code blocks reads from a csv file where values are separated with ‘;’ ?

Options :

A : spark.read.format("csv").option("header", "true").option(“inferSchema”, “true”).option(“sep”, “true”).toDf(file)

B : spark.read.format("csv").option("header", "true").option(“inferSchema”, “true”).load(file)

C : spark.read.format("csv").option("header", "true").option(“inferSchema”, “true”).option(“sep”, “;”).load(file)

D : spark.load.format("csv").option("header", "true").option(“inferSchema”, “true”).read(file)

Answer: C

Question 3

Which of the following 3 DataFrame operations are classified as a wide transformation ? Choose 3 answers:

Options :

A : orderBy()

B : drop()

C : repartition()

D : distinct()

E : filter()

F : cache()

Answer: A,C,D

Question 4

What are the possible strategies in order to decrease garbage collection time ?

Options :

A : Persist objects in serialized form

B : Create fewer objects

C : Increase java heap space size

Answer: A,B,C

Question 5

We have an unmanaged table “my_table”

If we run the code block down below

spark.sql(“DROP TABLE IF EXISTS my_table”)

What will happen to data in my_table ?

Options :

A : No data will be removed but you will no longer be able to refer to this data by the table name.

B : The data will be dropped also

C : Spark will remove the table and also associated views

D : This is not a valid code block

Answer: A