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

Given the code block down below, a database test and a dataframe containing nulls, identify the error.

1. spark.udf.register("strlen", (s: String) => s.length)

2. Spark.sql("select s from test where s is not null and strlen(s) > 1")

Options :

A : This WHERE clause does not guarantee the strlen UDF to be invoked after filtering out nulls. So we will have null pointer

B : We need to create the function first and then pass it to udf.register

C : We need to use function ‘query’ instead of ‘sql’ to query table test.

Answer: A

Question 3

Given an instance of SparkSession named spark, and the following DataFrame named df:

1. val simpleData = Seq(

2. ("James","Sales","NY",90000,34,10000),

3. ("Michael","Sales","NY",86000,56,20000),

4. ("Robert","Sales","CA",81000,30,23000),

5. ("Maria","Finance","CA",90000,24,23000),

6. ("Raman","Finance","CA",99000,40,24000),

7. ("Scott","Finance","NY",83000,36,19000),

8. ("Jen","Finance","NY",79000,53,15000),

9. ("Jeff","Marketing","CA",80000,25,18000),

10. ("Kumar","Marketing","NY",91000,50,21000)

11. )

val df = spark.createDataFrame(simpleData).toDf("employee_name","department","state","salary","age","bonus")

Choose the right code block which will produce the following result:

1. +----------+-----------+

2. |department|sum(salary)

3.

4. +----------+-----------+

5. Sales |257000

6. Finance |351000

7. Marketing |171000

8.

9. +----------+-----------+

Options :

A : df.groupBy("department").agg("salary").show()

B : df.groupBy(department).sum(salary).show()

C : df.groupBy("department").sumAll("salary").show()

D : df.reduce("department").sum("salary").show()

E : df.groupBy("department").sum("salary").show()

Answer: A

Question 4

Which of the following code blocks stores a part of the data in DataFrame itemsDf on executors?

Options :

A : itemsDf.cache().count()

B : itemsDf.cache(eager=True)

C : cache(itemsDf)

D : itemsDf.cache().filter()

E : itemsDf.rdd.storeCopy()

Answer: A

Question 5

Which of the following code blocks returns DataFrame transactionsDf sorted in descending order by column predError, showing missing values last?

Options :

A : transactionsDf.sort(asc_nulls_last("predError"))

B : transactionsDf.orderBy("predError").desc_nulls_last()

C : transactionsDf.sort("predError", ascending=False)

D : transactionsDf.desc_nulls_last("predError")

E : transactionsDf.orderBy("predError").asc_nulls_last()

Answer: C

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