Cloudera CCA175 CCA Spark and Hadoop Developer Exam Practice Test

Problem Scenario 90 : You have been given below two files

course.txt

id,course

1,Hadoop

2,Spark

3,HBase

fee.txt

id,fee

2,3900

3,4200

4,2900

Accomplish the following activities.

1. Select all the courses and their fees , whether fee is listed or not.

2. Select all the available fees and respective course. If course does not exists still list the fee

3. Select all the courses and their fees , whether fee is listed or not. However, ignore records having fee as null.

Problem Scenario 74 : You have been given MySQL DB with following details.

user=retail_dba

password=cloudera

database=retail_db

table=retail_db.orders

table=retail_db.order_items

jdbc URL = jdbc:mysql://quickstart:3306/retail_db

Columns of order table : (orderjd , order_date , ordercustomerid, order status}

Columns of orderjtems table : (order_item_td , order_item_order_id , order_item_product_id, order_item_quantity,order_item_subtotal,order_item_product_price)

Please accomplish following activities.

1. Copy "retaildb.orders" and "retaildb.orderjtems" table to hdfs in respective directory p89_orders and p89_order_items .

2. Join these data using orderjd in Spark and Python

3. Now fetch selected columns from joined data Orderld, Order date and amount collected on this order.

4. Calculate total order placed for each date, and produced the output sorted by date.

Problem Scenario 73 : You have been given data in json format as below.

{"first_name":"Ankit", "last_name":"Jain"}

{"first_name":"Amir", "last_name":"Khan"}

{"first_name":"Rajesh", "last_name":"Khanna"}

{"first_name":"Priynka", "last_name":"Chopra"}

{"first_name":"Kareena", "last_name":"Kapoor"}

{"first_name":"Lokesh", "last_name":"Yadav"}

Do the following activity

1. create employee.json file locally.

2. Load this file on hdfs

3. Register this data as a temp table in Spark using Python.

4. Write select query and print this data.

5. Now save back this selected data in json format.

Problem Scenario 70 : Write down a Spark Application using Python, In which it read a file "Content.txt" (On hdfs) with following content. Do the word count and save the results in a directory called "problem85" (On hdfs)

Content.txt

Hello this is ABCTECH.com

This is XYZTECH.com

Apache Spark Training

This is Spark Learning Session

Spark is faster than MapReduce

Problem Scenario 69 : Write down a Spark Application using Python,

In which it read a file "Content.txt" (On hdfs) with following content.

And filter out the word which is less than 2 characters and ignore all empty lines.

Once doen store the filtered data in a directory called "problem84" (On hdfs)

Content.txt

Hello this is ABCTECH.com

This is ABYTECH.com

Apache Spark Training

This is Spark Learning Session

Spark is faster than MapReduce

Problem Scenario 52 : You have been given below code snippet.

val b = sc.parallelize(List(1,2,3,4,5,6,7,8,2,4,2,1,1,1,1,1))

Operation_xyz

Write a correct code snippet for Operation_xyz which will produce below output. scalaxollection.Map[lnt,Long] = Map(5 -> 1, 8 -> 1, 3 -> 1, 6 -> 1, 1 -> S, 2 -> 3, 4 -> 2, 7 -> 1)

Problem Scenario 51 : You have been given below code snippet.

val a = sc.parallelize(List(1, 2,1, 3), 1)

val b = a.map((_, "b"))

val c = a.map((_, "c"))

Operation_xyz

Write a correct code snippet for Operationxyz which will produce below output.

Output:

Array[(lnt, (lterable[String], lterable[String]))] = Array(

(2,(ArrayBuffer(b),ArrayBuffer(c))),

(3,(ArrayBuffer(b),ArrayBuffer(c))),

(1,(ArrayBuffer(b, b),ArrayBuffer(c, c)))

)