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Cloudera CCA Spark and Hadoop Developer Sample Questions:

Question 1

CORRECT TEXT
Problem Scenario 67 : You have been given below code snippet.
lines = sc.parallelize(['lts fun to have fun,','but you have to know how.'])
M = lines.map( lambda x: x.replace(',7 ').replace('.',' 'J.replaceC-V ').lower()) r2 = r1.flatMap(lambda x: x.split()) r3 = r2.map(lambda x: (x, 1)) operation1
r5 = r4.map(lambda x:(x[1],x[0]))
r6 = r5.sortByKey(ascending=False)
r6.take(20)
Write a correct code snippet for operationl which will produce desired output, shown below.
[(2, 'fun'), (2, 'to'), (2, 'have'), (1, its'), (1, 'know1), (1, 'how1), (1, 'you'), (1, 'but')]

Question 2

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Problem Scenario 81 : You have been given MySQL DB with following details. You have been given following product.csv file product.csv productID,productCode,name,quantity,price
1001,PEN,Pen Red,5000,1.23
1002,PEN,Pen Blue,8000,1.25
1003,PEN,Pen Black,2000,1.25
1004,PEC,Pencil 2B,10000,0.48
1005,PEC,Pencil 2H,8000,0.49
1006,PEC,Pencil HB,0,9999.99
Now accomplish following activities.
1 . Create a Hive ORC table using SparkSql
2 . Load this data in Hive table.
3 . Create a Hive parquet table using SparkSQL and load data in it.

Question 3

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Problem Scenario 79 : 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 products table : (product_id | product categoryid | product_name | product_description | product_prtce | product_image )
Please accomplish following activities.
1 . Copy "retaildb.products" table to hdfs in a directory p93_products
2 . Filter out all the empty prices
3 . Sort all the products based on price in both ascending as well as descending order.
4 . Sort all the products based on price as well as product_id in descending order.
5 . Use the below functions to do data ordering or ranking and fetch top 10 elements top() takeOrdered() sortByKey()

Question 4

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Problem Scenario 49 : You have been given below code snippet (do a sum of values by key}, with intermediate output.
val keysWithValuesList = Array("foo=A", "foo=A", "foo=A", "foo=A", "foo=B", "bar=C",
"bar=D", "bar=D")
val data = sc.parallelize(keysWithValuesl_ist}
//Create key value pairs
val kv = data.map(_.split("=")).map(v => (v(0), v(l))).cache()
val initialCount = 0;
val countByKey = kv.aggregateByKey(initialCount)(addToCounts, sumPartitionCounts)
Now define two functions (addToCounts, sumPartitionCounts) such, which will produce following results.
Output 1
countByKey.collect
res3: Array[(String, Int)] = Array((foo,5), (bar,3))
import scala.collection._
val initialSet = scala.collection.mutable.HashSet.empty[String]
val uniqueByKey = kv.aggregateByKey(initialSet)(addToSet, mergePartitionSets)
Now define two functions (addToSet, mergePartitionSets) such, which will produce following results.
Output 2:
uniqueByKey.collect
res4: Array[(String, scala.collection.mutable.HashSet[String])] = Array((foo,Set(B, A}},
(bar,Set(C, D}}}

Question 5

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Problem Scenario 77 : 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 : (orderid , order_date , order_customer_id, order_status)
Columns of ordeMtems table : (order_item_id , order_item_order_ld ,
order_item_product_id, order_item_quantity,order_item_subtotal,order_
item_product_price)
Please accomplish following activities.
1. Copy "retail_db.orders" and "retail_db.order_items" table to hdfs in respective directory p92_orders and p92 order items .
2 . Join these data using orderid in Spark and Python
3 . Calculate total revenue perday and per order
4. Calculate total and average revenue for each date. - combineByKey
-aggregateByKey

Solutions: