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Cloudera CCA175 Exam Dumps


Exam Code: CCA175
Exam Name: CCA Spark and Hadoop Developer Exam

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CCA175 Exam Sample Questions:



Problem Scenario 62 : You have been given below code snippet.val a = sc.parallelize(List("dogM, "tiger", "lion", "cat", "panther", "eagle"), 2)
val b = a.map(x => (x.length, x))
operation1
Write a correct code snippet for operationl which will produce desired output, shown below.
Array[(lnt, String)] = Array((3,xdogx), (5,xtigerx), (4,xlionx), (3,xcatx), (7,xpantherx),
(5,xeaglex))

Answer: See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
b.mapValuesf'x" + _ + "x").collect
mapValues [Pair] : Takes the values of a RDD that consists of two-component tuples, and
applies the provided function to transform each value. Tlien,.it.forms newtwo-componend
tuples using the key and the transformed value and stores them in a new RDD.





Problem Scenario 59 : You have been given below code snippet.
val x = sc.parallelize(1 to 20)
val y = sc.parallelize(10 to 30) operationl
z.collect
Write a correct code snippet for operationl which will produce desired output, shown below.
Array[lnt] = Array(16,12, 20,13,17,14,18,10,19,15,11)

Answer: See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
val z = x.intersection(y)
intersection : Returns the elements in the two RDDs which are the same.





Problem Scenario 78 : 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_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 "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 order_id in Spark and Python
3. Calculate total revenue perday and per customer
4. Calculate maximum revenue customer

Answer: See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
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_orderitems --m 1
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 orderitems/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(Mp92_orders") orderitems =
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
orders Key Value = 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 ordersKeyValue']
ordersPerDatePerCustomer = joinedData.map(lambda line: ((line[1][1].split(",")[1],
line[1][1].split(",M)[2]), float(line[1][0].split(",")[4]))) amountCollectedPerDayPerCustomer =
ordersPerDatePerCustomer.reduceByKey(lambda runningSum, amount: runningSum +
amount}
#(Out record format will be ((date,customer_id), totalAmount} for line in
amountCollectedPerDayPerCustomer.collect(): print(line)
#now change the format of record as (date,(customer_id,total_amount))
revenuePerDatePerCustomerRDD = amountCollectedPerDayPerCustomer.map(lambda
threeElementTuple: (threeElementTuple[0][0],
(threeElementTuple[0][1],threeElementTuple[1])))
for line in revenuePerDatePerCustomerRDD.collect():
print(line)
#Calculate maximum amount collected by a customer for each day
perDateMaxAmountCollectedByCustomer =
revenuePerDatePerCustomerRDD.reduceByKey(lambda runningAmountTuple,
newAmountTuple: (runningAmountTuple if runningAmountTuple[1] >=
newAmountTuple[1] else newAmountTuple})for line in perDateMaxAmountCollectedByCustomer\sortByKey().collect(): print(line)





Problem Scenario 36 : You have been given a file named spark8/data.csv (type,name).
data.csv
1,Lokesh
2,Bhupesh
2,Amit
2,Ratan
2,Dinesh
1,Pavan
1,Tejas
2,Sheela
1,Kumar
1,Venkat
1. Load this file from hdfs and save it back as (id, (all names of same type)) in results
directory. However, make sure while saving it should be

Answer: See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Create file in hdfs (We will do using Hue). However, you can first create in local
filesystem and then upload it to hdfs.
Step 2 : Load data.csv file from hdfs and create PairRDDs
val name = sc.textFile("spark8/data.csv")
val namePairRDD = name.map(x=> (x.split(",")(0),x.split(",")(1)))
Step 3 : Now swap namePairRDD RDD.
val swapped = namePairRDD.map(item => item.swap)
Step 4 : Now combine the rdd by key.
val combinedOutput = namePairRDD.combineByKey(List(_), (x:List[String], y:String) => y ::
x, (x:List[String], y:List[String]) => x ::: y)
Step 5 : Save the output as a Text file and output must be written in a single file.
:ombinedOutput.repartition(1).saveAsTextFile("spark8/result.txt")





Problem Scenario 26 : You need to implement near real time solutions for collecting
information when submitted in file with below information. You have been given below
directory location (if not available than create it) /tmp/nrtcontent. Assume your departments
upstream service is continuously committing data in this directory as a new file (not stream
of data, because it is near real time solution). As soon as file committed in this directory
that needs to be available in hdfs in /tmp/flume location
Data
echo "I am preparing for CCA175 from ABCTECH.com" > /tmp/nrtcontent/.he1.txt
mv /tmp/nrtcontent/.he1.txt /tmp/nrtcontent/he1.txt
After few mins
echo "I am preparing for CCA175 from TopTech.com" > /tmp/nrtcontent/.qt1.txt
mv /tmp/nrtcontent/.qt1.txt /tmp/nrtcontent/qt1.txt
Write a flume configuration file named flumes.conf and use it to load data in hdfs with
following additional properties.
1. Spool /tmp/nrtcontent
2. File prefix in hdfs sholuld be events
3. File suffix should be Jog
4. If file is not commited and in use than it should have as prefix.
5. Data should be written as text to hdfs

Answer: See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Create directory mkdir /tmp/nrtcontent
Step 2 : Create flume configuration file, with below configuration for source, sink and
channel and save it in flume6.conf.
agent1 .sources = source1
agent1 .sinks = sink1
agent1.channels = channel1
agent1 .sources.source1.channels = channel1
agent1 .sinks.sink1.channel = channel1
agent1 .sources.source1.type = spooldir
agent1 .sources.source1.spoolDir = /tmp/nrtcontent
agent1 .sinks.sink1 .type = hdfs
agent1 .sinks.sink1.hdfs.path = /tmp/flume
agent1.sinks.sink1.hdfs.filePrefix = events
agent1.sinks.sink1.hdfs.fileSuffix = .log
agent1 .sinks.sink1.hdfs.inUsePrefix = _
agent1 .sinks.sink1.hdfs.fileType = Data Stream
Step 4 : Run below command which will use this configuration file and append data in
hdfs.
Start flume service:
flume-ng agent -conf /home/cloudera/flumeconf -conf-file
/home/cloudera/fIumeconf/fIume6.conf --name agent1
Step 5 : Open another terminal and create a file in /tmp/nrtcontent
echo "I am preparing for CCA175 from ABCTechm.com" > /tmp/nrtcontent/.he1.txt
mv /tmp/nrtcontent/.he1.txt /tmp/nrtcontent/he1.txt
After few mins
echo "I am preparing for CCA175 from TopTech.com" > /tmp/nrtcontent/.qt1.txt
mv /tmp/nrtcontent/.qt1.txt /tmp/nrtcontent/qt1.txt



How to Pass Cloudera CCA175 Exam?