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Cloudera CCA175 Exam — Your Questions, Answered

Cloudera CCA Spark and Hadoop Developer Sample Questions:

Question 1

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Problem Scenario 61 : You have been given below code snippet.
val a = sc.parallelize(List("dog", "salmon", "salmon", "rat", "elephant"), 3) val b = a.keyBy(_.length) val c = sc.parallelize(List("dog","cat","gnu","salmon","rabbit","turkey","wolf","bear","bee"), 3) val d = c.keyBy(_.length) operationl
Write a correct code snippet for operationl which will produce desired output, shown below.
Array[(lnt, (String, Option[String]}}] = Array((6,(salmon,Some(salmon))),
(6,(salmon,Some(rabbit))),
(6,(salmon,Some(turkey))), (6,(salmon,Some(salmon))), (6,(salmon,Some(rabbit))),
(6,(salmon,Some(turkey))), (3,(dog,Some(dog))), (3,(dog,Some(cat))),
(3,(dog,Some(dog))), (3,(dog,Some(bee))), (3,(rat,Some(dogg)), (3,(rat,Some(cat)j),
(3,(rat.Some(gnu))). (3,(rat,Some(bee))), (8,(elephant,None)))

Question 2

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Problem Scenario 47 : You have been given below code snippet, with intermediate output.
val z = sc.parallelize(List(1,2,3,4,5,6), 2)
// lets first print out the contents of the RDD with partition labels
def myfunc(index: Int, iter: lterator[(lnt)]): lterator[String] = {
iter.toList.map(x => "[partID:" + index + ", val: " + x + "]").iterator
}
//In each run , output could be different, while solving problem assume belowm output only.
z.mapPartitionsWithlndex(myfunc).collect
res28: Array[String] = Array([partlD:0, val: 1], [partlD:0, val: 2], [partlD:0, val: 3], [partlD:1, val: 4], [partlD:1, val: S], [partlD:1, val: 6])
Now apply aggregate method on RDD z , with two reduce function , first will select max value in each partition and second will add all the maximum values from all partitions.
Initialize the aggregate with value 5. hence expected output will be 16.

Question 3

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Problem Scenario 40 : You have been given sample data as below in a file called spark15/file1.txt
3070811,1963,1096,,"US","CA",,1,
3022811,1963,1096,,"US","CA",,1,56
3033811,1963,1096,,"US","CA",,1,23
Below is the code snippet to process this tile.
val field= sc.textFile("spark15/f ilel.txt")
val mapper = field.map(x=> A)
mapper.map(x => x.map(x=> {B})).collect
Please fill in A and B so it can generate below final output
Array(Array(3070811,1963,109G, 0, "US", "CA", 0,1, 0)
,Array(3022811,1963,1096, 0, "US", "CA", 0,1, 56)
,Array(3033811,1963,1096, 0, "US", "CA", 0,1, 23)
)

Question 4

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Problem Scenario 7 : You have been given following mysql database details as well as other info.
user=retail_dba
password=cloudera
database=retail_db
jdbc URL = jdbc:mysql://quickstart:3306/retail_db
Please accomplish following.
1. Import department tables using your custom boundary query, which import departments between 1 to 25.
2 . Also make sure each tables file is partitioned in 2 files e.g. part-00000, part-00002
3 . Also make sure you have imported only two columns from table, which are department_id,department_name

Question 5

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Problem Scenario 87 : You have been given below three files
product.csv (Create this file in hdfs)
productID,productCode,name,quantity,price,supplierid
1 001,PEN,Pen Red,5000,1.23,501
1 002,PEN,Pen Blue,8000,1.25,501
1003,PEN,Pen Black,2000,1.25,501
1004,PEC,Pencil 2B,10000,0.48,502
1005,PEC,Pencil 2H,8000,0.49,502
1006,PEC,Pencil HB,0,9999.99,502
2001,PEC,Pencil 3B,500,0.52,501
2002,PEC,Pencil 4B,200,0.62,501
2003,PEC,Pencil 5B,100,0.73,501
2004,PEC,Pencil 6B,500,0.47,502
supplier.csv
supplierid,name,phone
501,ABC Traders,88881111
502,XYZ Company,88882222
503,QQ Corp,88883333
products_suppliers.csv
productID,supplierID
2001,501
2002,501
2003,501
2004,502
2001,503
Now accomplish all the queries given in solution.
Select product, its price , its supplier name where product price is less than 0.6 using
SparkSQL

Solutions: