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CCA Spark and Hadoop Developer Exam Questions and Answers

Questions 1

Problem Scenario 87 : You have been given below three files

product.csv (Create this file in hdfs)

productID,productCode,name,quantity,price,supplierid

1001,PEN,Pen Red,5000,1.23,501

1002,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

Options:

Answer:

See the explanation for Step by Step Solution and configuration.

Explanation:

Solution :

Step 1:

hdfs dfs -mkdir sparksql2

hdfs dfs -put product.csv sparksq!2/

hdfs dfs -put supplier.csv sparksql2/

hdfs dfs -put products_suppliers.csv sparksql2/

Step 2 : Now in spark shell

// this Is used to Implicitly convert an RDD to a DataFrame.

import sqlContext.impIicits._

// Import Spark SQL data types and Row.

import org.apache.spark.sql._

// load the data into a new RDD

val products = sc.textFile("sparksql2/product.csv")

val supplier = sc.textFileC'sparksq^supplier.csv")

val prdsup = sc.textFile("sparksql2/products_suppliers.csv"}

// Return the first element in this RDD

products.fi rst()

supplier.first{).

prdsup.first()

//define the schema using a case class

case class Product(productid: Integer, code: String, name: String, quantity:lnteger, price: Float, supplierid:lnteger)

case class Suplier(supplierid: Integer, name: String, phone: String)

case class PRDSUP(productid: Integer.supplierid: Integer)

// 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,p(5).toint))

val supRDD = supplier.map(_.split(",")).map(p => Suplier(p(0).tolnt,p(1),p(2)))

val prdsupRDD = prdsup.map(_.split(",")).map(p => PRDSUP(p(0).tolnt,p(1}.tolnt}}

prdRDD.first()

prdRDD.count()

supRDD.first() supRDD.count()

prdsupRDD.first() prdsupRDD.count(}

// change RDD of Product objects to a DataFrame

val prdDF = prdRDD.toDF()

val supDF = supRDD.toDF()

val prdsupDF = prdsupRDD.toDF()

// register the DataFrame as a temp table prdDF.registerTempTablef'products")

supDF.registerTempTablef'suppliers")

prdsupDF.registerTempTablef'productssuppliers"}

//Select product, its price , its supplier name where product price is less than 0.6

val results = sqlContext.sql(......SELECT products.name, price, suppliers.name as sup_name FROM products JOIN suppliers ON products.supplierlD= suppliers.supplierlD WHERE price < 0.6......]

results. show()

Questions 2

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

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()

Options:

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=products -target-dir=p93_products -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 : 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("p93_products")

Step 4 : Filter empty prices, if exists

#filter out empty prices lines

nonemptyjines = productsRDD.filter(lambda x: len(x.split(",")[4]) > 0)

Step 5 : Now sort data based on product_price in order. sortedPriceProducts=nonempty_lines.map(lambdaline:(float(line.split(",")[4]),line.split(",")[2])).sortByKey()

for line in sortedPriceProducts.collect(): print(line)

Step 6 : Now sort data based on product_price in descending order. sortedPriceProducts=nonempty_lines.map(lambda line: (float(line.split(",")[4]),line.split(",")[2])).sortByKey(False)

for line in sortedPriceProducts.collect(): print(line)

Step 7 : Get highest price products name. sortedPriceProducts=nonemptyJines.map(lambda line : (float(line.split(",")[4]),line-split(,,,,,)[2]))-sortByKey(False).take(1)

print(sortedPriceProducts)

Step 8 : Now sort data based on product_price as well as product_id in descending order.

#Dont forget to cast string #Tuple as key ((price,id),name)

sortedPriceProducts=nonemptyJines.map(lambda line : ((float(line print(sortedPriceProducts)

Step 9 : Now sort data based on product_price as well as product_id in descending order, using top() function.

#Dont forget to cast string

#Tuple as key ((price,id),name)

sortedPriceProducts=nonemptyJines.map(lambda line: ((float(line.s^^

print(sortedPriceProducts)

Step 10 : Now sort data based on product_price as ascending and product_id in ascending order, using takeOrdered{) function.

#Dont forget to cast string

#Tuple as key ((price,id),name) sortedPriceProducts=nonemptyJines.map(lambda line: ((float(line.split(","}[4]},int(line.split(","}[0]}},line.split(","}[2]}}.takeOrdered(10, lambda tuple : (tuple[0][0],tuple[0][1]))

Step 11 : Now sort data based on product_price as descending and product_id in ascending order, using takeOrdered() function.

#Dont forget to cast string

#Tuple as key ((price,id},name)

#Using minus(-) parameter can help you to make descending ordering , only for numeric value.

sortedPrlceProducts=nonemptylines.map(lambda line: ((float(line.split(","}[4]},int(line.split(","}[0]}},line.split(","}[2]}}.takeOrdered(10, lambda tuple : (-tuple[0][0],tuple[0][1]}}

Questions 3

Problem Scenario 21 : You have been given log generating service as below.

startjogs (It will generate continuous logs)

tailjogs (You can check , what logs are being generated)

stopjogs (It will stop the log service)

Path where logs are generated using above service : /opt/gen_logs/logs/access.log

Now write a flume configuration file named flumel.conf , using that configuration file dumps logs in HDFS file system in a directory called flumel. Flume channel should have following property as well. After every 100 message it should be committed, use non-durable/faster channel and it should be able to hold maximum 1000 events

Solution :

Step 1 : Create flume configuration file, with below configuration for source, sink and channel.

#Define source , sink , channel and agent,

agent1 .sources = source1

agent1 .sinks = sink1

agent1.channels = channel1

# Describe/configure source1

agent1 .sources.source1.type = exec

agent1.sources.source1.command = tail -F /opt/gen logs/logs/access.log

## Describe sinkl

agentl .sinks.sinkl.channel = memory-channel

agentl .sinks.sinkl .type = hdfs

agentl .sinks.sink1.hdfs.path = flumel

agentl .sinks.sinkl.hdfs.fileType = Data Stream

# Now we need to define channell property.

agent1.channels.channel1.type = memory

agent1.channels.channell.capacity = 1000

agent1.channels.channell.transactionCapacity = 100

# Bind the source and sink to the channel

agent1.sources.source1.channels = channel1

agent1.sinks.sink1.channel = channel1

Step 2 : Run below command which will use this configuration file and append data in hdfs.

Start log service using : startjogs

Start flume service:

flume-ng agent -conf /home/cloudera/flumeconf -conf-file /home/cloudera/flumeconf/flumel.conf-Dflume.root.logger=DEBUG,INFO,console

Wait for few mins and than stop log service.

Stop_logs

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Cloudera CCA175 Exam Dumps Questions and Answers

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CCA Spark and Hadoop Developer Exam Questions and Answers

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Cloudera CCA175 Exam Dumps Questions and Answers

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Other Cloudera Certification Exams

CCA Spark and Hadoop Developer Exam Questions and Answers

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Explanation:

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Answer:

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