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Cloudera CCA Spark and Hadoop Developer Exam Dumps

Last Updated : Apr 14, 2024

Total Questions : 96

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Following are some CCA175 Exam Questions for Review

Question 1

Problem Scenario 96 : Your spark application required extra Java options as below. -XX:+PrintGCDetails-XX:+PrintGCTimeStamps

Please replace the XXX values correctly

./bin/spark-submit --name "My app" --master local[4] --conf spark.eventLog.enabled=talse --conf XXX hadoopexam.jar

ASolution
XXX: Mspark.executoi\extraJavaOptions=-XX:+PrintGCDetails -XX:+PrintGCTimeStamps'
Notes: ./bin/spark-submit \
--class <maln-class>
--master <master-url> \
--deploy-mode <deploy-mode> \
-conf <key>=<value> \
... # other options

\
[application-arguments]
Here, conf is used to pass the Spark related contigs which are required for the application to run like any specific property(executor memory) or if you want to override the default property which is set in Spark-default.conf.

BSolution
XXX: Mspark.executoi\extraJavaOptions=-XX:+PrintGCDetails -XX:+PrintGCTimeStamps'
Notes: ./bin/spark-submit \

Question 2

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.

ASolution :
Step 1 : Create this tile in HDFS under following directory (Without header} /user/cloudera/he/exam/task1/productcsv
Step 2 : Now using Spark-shell read the file as RDD
// load the data into a new RDD
val products = sc.textFile('/user/cloudera/he/exam/task1/product.csv')
// Return the first element in this RDD
prod u cts.fi rst()
Step 3 : Now define the schema using a case class
case class Product(productid: Integer, code: String, name: String, quantity:lnteger, price: Float)
Step 4 : create an RDD of Product objects
val prdRDD = products.map(_.split(',')).map(p => Product(p(0).tolnt,p(1),p(2),p(3}.tolnt,p(4}.toFloat))
prdRDD.first()
prdRDD.count()
Step 5 : Now create data frame val prdDF = prdRDD.toDF()
step 6: Now create table using data stored in warehouse directory. With the help of hive.
hive
show tables
CREATE EXTERNAL TABLE products (productid int,code string,name string .quantity int, price float}
STORED AS ore
LOCATION 7user/hive/warehouse/product_orc_table';
Step 7 : Now create a parquet table
import org.apache.spark.sql.SaveMode
prdDF.write.mode(SaveMode.Overwrite).format('parquet').saveAsTable('product_parquet_table')
Step 8 : Now create table using this
CREATE EXTERNAL TABLE products_parquet (productid int,code string,name string .quantity int, price float}
STORED AS parquet
LOCATION 7user/hive/warehouse/product_parquet_table';
Step 9 : Check data has been loaded or not.
Select * from products;
Select * from products_parquet;

BSolution :
Step 1 : Create this tile in HDFS under following directory (Without header} /user/cloudera/he/exam/task1/productcsv
Step 2 : Now using Spark-shell read the file as RDD
// load the data into a new RDD
val products = sc.textFile('/user/cloudera/he/exam/task1/product.csv')
// Return the first element in this RDD
prod u cts.fi rst()
Step 3 : Now define the schema using a case class
case class Product(productid: Integer, code: String, name: String, quantity:lnteger, price: Float)
Step 4 : create an RDD of Product objects
val prdRDD = products.map(_.split(',')).map(p => Product(p(0).tolnt,p(1),p(2),p(3}.tolnt,p(4}.toFloat))
prdRDD.first()
prdRDD.count()
Step 5 : Now create data frame val prdDF = prdRDD.toDF()
Step 6 : Now store data in hive warehouse directory. (However, table will not be created } import org.apache.spark.sql.SaveMode prdDF.write.mode(SaveMode.Overwrite).format('orc').saveAsTable('product_orc_table')
step 7: Now create table using data stored in warehouse directory. With the help of hive.
hive
show tables
CREATE EXTERNAL TABLE products (productid int,code string,name string .quantity int, price float}
STORED AS ore
LOCATION 7user/hive/warehouse/product_orc_table';
Step 8 : Now create a parquet table
import org.apache.spark.sql.SaveMode
prdDF.write.mode(SaveMode.Overwrite).format('parquet').saveAsTable('product_parquet_table')
Step 9 : Now create table using this
CREATE EXTERNAL TABLE products_parquet (productid int,code string,name string .quantity int, price float}
STORED AS parquet
LOCATION 7user/hive/warehouse/product_parquet_table';
Step 10 : Check data has been loaded or not.
Select * from products;
Select * from products_parquet;

Question 3

Problem Scenario 80 : You have been given MySQL DB with following details.

user=retail_dba

password=cloudera

database=retail_db

table=retail_db.products

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

Please accomplish following activities.

1. Copy "retaildb.products" table to hdfs in a directory p93_products

2. Now sort the products data sorted by product price per category, use productcategoryid colunm to group by category

ASolution :
Step 1 : Import Single table .
sqoop import --connect jdbc:mysql://quickstart:3306/retail_db -username=retail_dba -password=cloudera -table=products --target-dir=p93
Note : Please check you dont have space between before or after '=' sign. Sqoop uses the MapReduce framework to copy data from RDBMS to hdfs
Step 2 : Step 2 : Read the data from one of the partition, created using above command, hadoop fs -cat p93_products/part-m-00000
Step 3 : Load this directory as RDD using Spark and Python (Open pyspark terminal and do following}. productsRDD = sc.textFile(Mp93_products')
Step 4 : Filter empty prices, if exists
#filter out empty prices lines
Nonempty_lines = productsRDD.filter(lambda x: len(x.split(',')[4]) > 0)
Step 5 : Create data set like (categroyld, (id,name,price)
mappedRDD = nonempty_lines.map(lambda line: (line.split(',')[1], (line.split(',')[0], line.split(',')[2], float(line.split(',')[4]))))
tor line in mappedRDD.collect(): print(line)
Step 6 : Now groupBy the all records based on categoryld, which a key on mappedRDD it will produce output like (categoryld, iterable of all linesfor a key/categoryld)
groupByCategroyld = mappedRDD.groupByKey() for line in groupByCategroyld.collect(): print(line)
step 7 : Now sort the data in each category based on price in ascending order.
# sorted is a function to sort an iterable, we can also specify, what would be the Key on which we want to sort in this case we have price onwhich it needs to be sorted.
groupByCategroyld.map(lambda tuple: sorted(tuple[1], key=lambda tupleValue: tupleValue[2])).take(5)
Step 8 : Now sort the data in each category based on price in descending order.
# sorted is a function to sort an iterable, we can also specify, what would be the Key on which we want to sort in this case we have price which it needs to be sorted.
on groupByCategroyld.map(lambda tuple: sorted(tuple[1], key=lambda tupleValue: tupleValue[2] , reverse=True)).take(5)

BSolution :
Step 1 : Import Single table .
sqoop import --connect jdbc:mysql://quickstart:3306/retail_db -username=retail_dba -password=cloudera -table=products --target-dir=p93
Note : Please check you dont have space between before or after '=' sign. Sqoop uses the MapReduce framework to copy data from RDBMS to hdfs
Step 2 : Step 2 : Read the data from one of the partition, created using above command, hadoop fs -cat p93_products/part-m-00000
Step 3 : Load this directory as RDD using Spark and Python (Open pyspark terminal and do following}. productsRDD = sc.textFile(Mp93_products')
Step 4 : Filter empty prices, if exists
#filter out empty prices lines
Nonempty_lines = productsRDD.filter(lambda x: len(x.split(',')[4]) > 0)
Step 5 : Create data set like (categroyld, (id,name,price)
mappedRDD = nonempty_lines.map(lambda line: (line.split(',')[1], (line.split(',')[0], line.split(',')[2], float(line.split(',')[4]))))
tor line in mappedRDD.collect(): print(line)
step 6 : Now sort the data in each category based on price in ascending order.
# sorted is a function to sort an iterable, we can also specify, what would be the Key on which we want to sort in this case we have price onwhich it needs to be sorted.
groupByCategroyld.map(lambda tuple: sorted(tuple[1], key=lambda tupleValue: tupleValue[2])).take(5)
Step 7 : Now sort the data in each category based on price in descending order.
# sorted is a function to sort an iterable, we can also specify, what would be the Key on which we want to sort in this case we have price which it needs to be sorted.
on groupByCategroyld.map(lambda tuple: sorted(tuple[1], key=lambda tupleValue: tupleValue[2] , reverse=True)).take(5)

Question 4

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

ASolution :
Step 1 : Import Single table .
sqoop import --connect jdbc:mysql://quickstart:3306/retail_db -username=retail_dba -password=cloudera -table=orders --target-dir=p92_orders --m 1
sqoop import --connect jdbc:mysql://quickstart:3306/retail_db --username=retail_dba -password=cloudera -table=order_items --target-dir=p92_order_items --m1
Note : Please check you dont have space between before or after '=' sign. Sqoop uses the MapReduce framework to copy data from RDBMS to hdfs
Step 2 : Read the data from one of the partition, created using above command, hadoop fs -cat p92_orders/part-m-00000 hadoop fs -cat p92_order_items/part-m-00000
Step 3 : Load these above two directory as RDD using Spark and Python (Open pyspark terminal and do following). orders = sc.textFile('p92_orders') orderltems = sc.textFile('p92_order_items')
Step 4 : Convert RDD into key value as (orderjd as a key and rest of the values as a value)
#First value is orderjd
ordersKeyValue = orders.map(lambda line: (int(line.split(',')[0]), line))
#Second value as an Orderjd
orderltemsKeyValue = orderltems.map(lambda line: (int(line.split(',')[1]), line))
Step 5 : Join both the RDD using orderjd
joinedData = orderltemsKeyValue.join(ordersKeyValue)
#print the joined data
for line in joinedData.collect():
print(line)
Format of joinedData as below.
[Orderld, 'All columns from orderltemsKeyValue', 'All columns from orders Key Value']
Step 6 : Now fetch selected values Orderld, Order date and amount collected on this order.
//Retruned row will contain ((order_date,order_id),amout_collected)
revenuePerDayPerOrder = joinedData.map(lambda row: ((row[1][1].split(M,M)[1],row[0]}, float(row[1][0].split(',')[4])))
#print the result
for line in revenuePerDayPerOrder.collect():
print(line)
Step 7 : Now calculate total revenue perday and per order
A . Using reduceByKey
totalRevenuePerDayPerOrder = revenuePerDayPerOrder.reduceByKey(lambda runningSum, value: runningSum + value)
for line in totalRevenuePerDayPerOrder.sortByKey().collect(): print(line)
#Generate data as (date, amount_collected) (Ignore ordeMd)
dateAndRevenueTuple = totalRevenuePerDayPerOrder.map(lambda line: (line[0][0], line[1]))
for line in dateAndRevenueTuple.sortByKey().collect(): print(line)
Step 8 : Calculate total amount collected for each day. And also calculate number of days. #Generate output as (Date, Total Revenue for date, total_number_of_dates)
#Line 1 : it will generate tuple (revenue, 1)
#Line 2 : Here, we will do summation for all revenues at the same time another counter to maintain number of records.
#Line 3 : Final function to merge all the combiner
totalRevenueAndTotalCount = dateAndRevenueTuple.combineByKey( \
lambda revenue: (revenue, 1), \
lambda revenueSumTuple, amount: (revenueSumTuple[0] + amount, revenueSumTuple[1] + 1), \
lambda tuplel, tuple2: (round(tuple1[0] + tuple2[0], 2}, tuple1[1] + tuple2[1]) \
for line in totalRevenueAndTotalCount.collect(): print(line)
Step 9 : Now calculate average for each date
averageRevenuePerDate = totalRevenueAndTotalCount.map(lambda threeElements: (threeElements[0], threeElements[1][0]/threeElements[1][1]}}
for line in averageRevenuePerDate.collect(): print(line)
Step 10 : Using aggregateByKey
#line 1 : (Initialize both the value, revenue and count)
#line 2 : runningRevenueSumTuple (Its a tuple for total revenue and total record count for each date)
#line 3 : Summing all partitions revenue and count
totalRevenueAndTotalCount = dateAndRevenueTuple.aggregateByKey( \
(0,0), \
lambda runningRevenueSumTuple, revenue: (runningRevenueSumTuple[0] + revenue, runningRevenueSumTuple[1] + 1), \
lambda tupleOneRevenueAndCount, tupleTwoRevenueAndCount: (tupleOneRevenueAndCount[0] + tupleTwoRevenueAndCount[0], tupleOneRevenueAndCount[1] + tupleTwoRevenueAndCount[1]) \
)
for line in totalRevenueAndTotalCount.collect(): print(line)
Step 11 : Calculate the average revenue per date
averageRevenuePerDate = totalRevenueAndTotalCount.map(lambda threeElements: (threeElements[0], threeElements[1][0]/threeElements[1][1]))
for line in averageRevenuePerDate.collect(): print(line)

BSolution :
Step 1 : Import Single table .
sqoop import --connect jdbc:mysql://quickstart:3306/retail_db -username=retail_dba -password=cloudera -table=orders --target-dir=p92_orders --m 1
sqoop import --connect jdbc:mysql://quickstart:3306/retail_db --username=retail_dba -password=cloudera -table=order_items --target-dir=p92_order_items --m1
Note : Please check you dont have space between before or after '=' sign. Sqoop uses the MapReduce framework to copy data from RDBMS to hdfs
Step 2 : Read the data from one of the partition, created using above command, hadoop fs -cat p92_orders/part-m-00000 hadoop fs -cat p92_order_items/part-m-66666
Step 3 : Load these above two directory as RDD using Spark and Python (Open pyspark terminal and do following). orders = sc.textFile('p92_orders') orderltems = sc.textFile('p92_order_items')
Step 4 : Convert RDD into key value as (orderjd as a key and rest of the values as a value)
#First value is orderjd
ordersKeyValue = orders.map(lambda line: (int(line.split(',')[0]), line))
#Second value as an Orderjd
orderltemsKeyValue = orderltems.map(lambda line: (int(line.split(',')[1]), line))
Step 5 : Join both the RDD using orderjd
joinedData = orderltemsKeyValue.join(ordersKeyValue)
#print the joined data
for line in joinedData.collect():
print(line)
Format of joinedData as below.
[Orderld, 'All columns from orderltemsKeyValue', 'All columns from orders Key Value']
Step 6 : Now fetch selected values Orderld, Order date and amount collected on this order.
//Retruned row will contain ((order_date,order_id),amout_collected)
revenuePerDayPerOrder = joinedData.map(lambda row: ((row[1][1].split(M,M)[1],row[0]},
print(line)
Step 7 : Now calculate total revenue perday and per order
A . Using reduceByKey
totalRevenuePerDayPerOrder = revenuePerDayPerOrder.reduceByKey(lambda runningSum, value: runningSum + value)
for line in totalRevenuePerDayPerOrder.sortByKey().collect(): print(line)
#Generate data as (date, amount_collected) (Ignore ordeMd)
dateAndRevenueTuple = totalRevenuePerDayPerOrder.map(lambda line: (line[0][0], line[1]))
for line in dateAndRevenueTuple.sortByKey().collect(): print(line)
Step 8 : Calculate total amount collected for each day. And also calculate number of days. #Generate output as (Date, Total Revenue for date, total_number_of_dates)
#Line 1 : it will generate tuple (revenue, 1)
#Line 2 : Here, we will do summation for all revenues at the same time another counter to maintain number of records.
#Line 3 : Final function to merge all the combiner
totalRevenueAndTotalCount = dateAndRevenueTuple.combineByKey( \
lambda revenue: (revenue, 1), \
lambda revenueSumTuple, amount: (revenueSumTuple[0] + amount, revenueSumTuple[1] + 1), \
lambda tuplel, tuple2: (round(tuple1[0] + tuple2[0], 2}, tuple1[1] + tuple2[1]) \
for line in totalRevenueAndTotalCount.collect(): print(line)
Step 9 : Now calculate average for each date
averageRevenuePerDate = totalRevenueAndTotalCount.map(lambda threeElements: (threeElements[0], threeElements[1][0]/threeElements[1][1]}}
for line in averageRevenuePerDate.collect(): print(line)
Step 10 : Using aggregateByKey
#line 1 : (Initialize both the value, revenue and count)
#line 2 : runningRevenueSumTuple (Its a tuple for total revenue and total record count for each date)
#line 3 : Summing all partitions revenue and count
totalRevenueAndTotalCount = dateAndRevenueTuple.aggregateByKey( \
(0,0), \
lambda runningRevenueSumTuple, revenue: (runningRevenueSumTuple[0] + revenue, runningRevenueSumTuple[1] + 1), \
lambda tupleOneRevenueAndCount, tupleTwoRevenueAndCount: (tupleOneRevenueAndCount[0] + tupleTwoRevenueAndCount[0], tupleOneRevenueAndCount[1] + tupleTwoRevenueAndCount[1]) \
)
for line in totalRevenueAndTotalCount.collect(): print(line)
Step 11 : Calculate the average revenue per date
averageRevenuePerDate = totalRevenueAndTotalCount.map(lambda threeElements: (threeElements[0], threeElements[1][0]/threeElements[1][1]))
for line in averageRevenuePerDate.collect(): print(line)

Question 5

Problem Scenario 76 : 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 , ordercustomerid, order_status}

.....

Please accomplish following activities.

1. Copy "retail_db.orders" table to hdfs in a directory p91_orders.

2. Once data is copied to hdfs, using pyspark calculate the number of order for each status.

3. Use all the following methods to calculate the number of order for each status. (You need to know all these functions and its behavior for real exam)

- countByKey()

-groupByKey()

- reduceByKey()

-aggregateByKey()

- combineByKey()

ASolution :
Step 1 : Import Single table
sqoop import --connect jdbc:mysql://quickstart:3306/retail_db --username=retail dba -password=cloudera -table=orders --target-dir=p91_orders
Note : Please check you dont have space between before or after '=' sign. Sqoop uses the MapReduce framework to copy data from RDBMS to hdfs
Step 2 : Read the data from one of the partition, created using above command, hadoop fs -cat p91_orders/part-m-00000
Step 3: countByKey #Number of orders by status allOrders = sc.textFile('p91_orders')
#Generate key and value pairs (key is order status and vale as an empty string keyValue = aIIOrders.map(lambda line: (line.split(',')[3], ''))
#Using countByKey, aggregate data based on status as a key output=keyValue.countByKey()Jtems()
for line in output: print(line)
Step 4 : groupByKey
#Generate key and value pairs (key is order status and vale as an one
keyValue = allOrders.map(lambda line: (line.split)',')[3], 1))
Step 5 : reduceByKey
#Generate key and value pairs (key is order status and vale as an one
keyValue = allOrders.map(lambda line: (line.split(','}[3], 1))
#Using countByKey, aggregate data based on status as a key output= keyValue.reduceByKey(lambda a, b: a + b)
tor line in output.collect(): print(line}
Step 6: aggregateByKey
#Generate key and value pairs (key is order status and vale as an one keyValue =
Step 7 : combineByKey
#Generate key and value pairs (key is order status and vale as an one
keyValue = allOrders.map(lambda line: (line.split(',')[3], line))
output=keyValue.combineByKey(lambda value: 1, lambda ace, value: acc+1, lambda ace, value: acc+value)
tor line in output.collect(): print(line)
#Watch Spark Professional Training provided by www.ABCTECH.com to understand more on each above functions. (These are very important functions for real exam)

BSolution :
Step 1 : Import Single table
sqoop import --connect jdbc:mysql://quickstart:3306/retail_db --username=retail dba -password=cloudera -table=orders --target-dir=p91_orders
Note : Please check you dont have space between before or after '=' sign. Sqoop uses the MapReduce framework to copy data from RDBMS to hdfs
Step 2 : Read the data from one of the partition, created using above command, hadoop fs -cat p91_orders/part-m-00000
Step 3: countByKey #Number of orders by status allOrders = sc.textFile('p91_orders')
#Generate key and value pairs (key is order status and vale as an empty string keyValue = aIIOrders.map(lambda line: (line.split(',')[3], ''))
#Using countByKey, aggregate data based on status as a key output=keyValue.countByKey()Jtems()
for line in output: print(line)
Step 4 : groupByKey
#Generate key and value pairs (key is order status and vale as an one
keyValue = allOrders.map(lambda line: (line.split)',')[3], 1))
#Using countByKey, aggregate data based on status as a key output= keyValue.groupByKey().map(lambda kv: (kv[0], sum(kv[1]}}}
tor line in output.collect(): print(line}
Step 5 : reduceByKey
#Generate key and value pairs (key is order status and vale as an one
keyValue = allOrders.map(lambda line: (line.split(','}[3], 1))
#Using countByKey, aggregate data based on status as a key output= keyValue.reduceByKey(lambda a, b: a + b)
tor line in output.collect(): print(line}
Step 6: aggregateByKey
#Generate key and value pairs (key is order status and vale as an one keyValue = allOrders.map(lambda line: (line.split(',')[3], line}}
output=keyValue.aggregateByKey(0, lambda a, b: a+1, lambda a, b: a+b}
for line in output.collect(): print(line}
Step 7 : combineByKey
#Generate key and value pairs (key is order status and vale as an one
keyValue = allOrders.map(lambda line: (line.split(',')[3], line))
output=keyValue.combineByKey(lambda value: 1, lambda ace, value: acc+1, lambda ace, value: acc+value)
tor line in output.collect(): print(line)
#Watch Spark Professional Training provided by www.ABCTECH.com to understand more on each above functions. (These are very important functions for real exam)

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