Question 1

If we want to create a constant integer 1 as a new column ‘new_column’ in a dataframe df, which code block we should select ?

Options :

A : df.withColumn(new_column, lit(1))

B : df.withColumn(”new_column”, lit(“1”))

C : df.withColumnRenamed('new_column', lit(1))

D : df.withColumn(“new_column”, 1)

E : df.withColumn(“new_column”, lit(1))

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

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

Question 4

When joining two dataframes, if there is a need to evaluate the keys in both of the DataFrames or tables and include all rows from the right DataFrame as well as any rows in the left DataFrame that have a match in the righ DataFrame also If there is no equivalent row in the left DataFrame, we want to instert null: which join type should we select ? df1.join(person, joinExpression, joinType)

Options :

A : val joinType = “rightAnti”

B : val joinType = “rightSemi”

C : val joinType = “rightOuter”

D : val joinType = “right_outer”

Answer: D

Question 5

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

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