Question 1

There is a global temp view named ‘my_global_view’. If I want to query this view within spark, which command do we need to choose ?

Options :

A : spark.read.table("global_temp.my_global_view")

B : spark.read.view("global_temp.my_global_view")

C : spark.read.view("my_global_view")

D : spark.read.table("my_global_view")

Answer: A

Question 2

Which of the following is the deepest level in Spark's execution hierarchy?

Options :

A : Job

B : Task

C : Executor

D : Slot

E : Stage

Answer: B

Question 3

Which of the following are valid execution modes?

Options :

A : Kubernetes, Local, Client

B : Client, Cluster, Local

C : Server, Standalone, Client

D : Cluster, Server, Local

E : Standalone, Client, Cluster

Answer: B

Question 4

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 |2570006. Finance |3510007. Marketing |1710008.9. +----------+-----------+

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 5

The code block displayed below contains an error. The code block should trigger Spark to cache DataFrametransactionsDf in executor memory where available, writing to disk where insufficientexecutor memory is available, in a fault-tolerant way. Find the error.Code block:transactionsDf.persist(StorageLevel.MEMORY_AND_DISK)

Options :

A : Caching is not supported in Spark, data are always recomputed.

B : Data caching capabilities can be accessed through the spark object, but not through the DataFrame API.

C : The storage level is inappropriate for fault-tolerant storage.

D : The code block uses the wrong operator for caching.

E : The DataFrameWriter needs to be invoked.

Answer: C

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