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Free CCA175 Exam Braindumps (page: 6)

Page 5 of 25
PDF with 96 Questions

Problem Scenario 24 : You have been given below comma separated employee information.
Data Set:
name, salary, sex, age
alok, 100000, male, 29
jatin, 105000, male, 32
yogesh, 134000, male, 39
ragini, 112000, female, 35
jyotsana, 129000, female, 39
valmiki, 123000, male, 29
Requirements:
Use the netcat service on port 44444, and nc above data line by line. Please do the following activities.

1. Create a flume conf file using fastest channel, which write data in hive warehouse
directory, in a table called flumemaleemployee (Create hive table as well tor given data).
2. While importing, make sure only male employee data is stored.

  1. See the explanation for Step by Step Solution and configuration.

Answer(s): A

Explanation:

Step 1: Create hive table for flumeemployee.'
CREATE TABLE flumemaleemployee
(
name string,
salary int,
sex string,
age int
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ', ';
Step 2: Create flume configuration file, with below configuration for source, sink and channel and save it in flume4.conf.
#Define source , sink, channel and agent.
agent1 .sources = source1
agent1 .sinks = sink1
agent1 .channels = channel1
# Describe/configure source1
agent1 .sources.source1.type = netcat
agent1 .sources.source1.bind = 127.0.0.1
agent1.sources.sourcel.port = 44444
#Define interceptors
agent1.sources.source1.interceptors=il
agent1 .sources.source1.interceptors.i1.type=regex_filter agent1 .sources.source1.interceptors.i1.regex=female agent1 .sources.source1.interceptors.i1.excludeEvents=true
## Describe sink1
agent1 .sinks, sinkl.channel = memory-channel
agent1.sinks.sink1.type = hdfs
agent1 .sinks, sinkl. hdfs. path = /user/hive/warehouse/flumemaleemployee hdfs-agent.sinks.hdfs-write.hdfs.writeFormat=Text
agentl .sinks.sink1.hdfs.fileType = Data Stream
# Now we need to define channel1 property.
agent1.channels.channel1.type = memory
agent1.channels.channell.capacity = 1000
agent1.channels.channel1.transactionCapacity = 100
# Bind the source and sink to the channel
agent1 .sources.source1.channels = channel1
agent1 .sinks.sink1.channel = channel1
Step 3: 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/flumeconf/flume4.conf --name agentl
Step 4: Open another terminal and use the netcat service, nc localhost 44444
Step 5: Enter data line by line.
alok, 100000, male, 29
jatin, 105000, male, 32
yogesh, 134000, male, 39
ragini, 112000, female, 35
jyotsana, 129000, female, 39
valmiki.123000.male.29
Step 6: Open hue and check the data is available in hive table or not.
Step 7: Stop flume service by pressing ctrl+c
Step 8: Calculate average salary on hive table using below query. You can use either hive command line tool or hue. select avg(salary) from flumeemployee;



Problem Scenario 27 : You need to implement near real time solutions for collecting information when submitted in file with below information.
Data
echo "IBM, 100, 20160104" >> /tmp/spooldir/bb/.bb.txt
echo "IBM, 103, 20160105" >> /tmp/spooldir/bb/.bb.txt
mv /tmp/spooldir/bb/.bb.txt /tmp/spooldir/bb/bb.txt
After few mins
echo "IBM, 100.2, 20160104" >> /tmp/spooldir/dr/.dr.txt
echo "IBM, 103.1, 20160105" >> /tmp/spooldir/dr/.dr.txt
mv /tmp/spooldir/dr/.dr.txt /tmp/spooldir/dr/dr.txt
Requirements:
You have been given below directory location (if not available than create it) /tmp/spooldir . You have a finacial subscription for getting stock prices from BloomBerg as well as
Reuters and using ftp you download every hour new files from their respective ftp site in directories /tmp/spooldir/bb and /tmp/spooldir/dr respectively.
As soon as file committed in this directory that needs to be available in hdfs in /tmp/flume/finance location in a single directory.
Write a flume configuration file named flume7.conf and use it to load data in hdfs with following additional properties .

1. Spool /tmp/spooldir/bb and /tmp/spooldir/dr
2. File prefix in hdfs sholuld be events
3. File suffix should be .log
4. If file is not commited and in use than it should have _ as prefix.
5. Data should be written as text to hdfs

  1. See the explanation for Step by Step Solution and configuration.

Answer(s): A

Explanation:

Solution :
Step 1: Create directory mkdir /tmp/spooldir/bb mkdir /tmp/spooldir/dr
Step 2: Create flume configuration file, with below configuration for
agent1.sources = source1 source2
agent1 .sinks = sink1
agent1.channels = channel1
agent1 .sources.source1.channels = channel1
agentl .sources.source2.channels = channell agent1 .sinks.sinkl.channel = channell agent1 .sources.source1.type = spooldir
agent1 .sources.sourcel.spoolDir = /tmp/spooldir/bb
agent1 .sources.source2.type = spooldir
agent1 .sources.source2.spoolDir = /tmp/spooldir/dr
agent1 .sinks.sink1.type = hdfs
agent1 .sinks.sink1.hdfs.path = /tmp/flume/finance
agent1-sinks.sink1.hdfs.filePrefix = events
agent1.sinks.sink1.hdfs.fileSuffix = .log
agent1 .sinks.sink1.hdfs.inUsePrefix = _
agent1 .sinks.sink1.hdfs.fileType = Data Stream
agent1.channels.channel1.type = file
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/fIume7.conf --name agent1
Step 5: Open another terminal and create a file in /tmp/spooldir/ echo "IBM, 100, 20160104" » /tmp/spooldir/bb/.bb.txt
echo "IBM, 103, 20160105" » /tmp/spooldir/bb/.bb.txt mv /tmp/spooldir/bb/.bb.txt /tmp/spooldir/bb/bb.txt
After few mins
echo "IBM, 100.2, 20160104" » /tmp/spooldir/dr/.dr.txt echo "IBM, 103.1, 20160105" »/tmp/spooldir/dr/.dr.txt mv /tmp/spooldir/dr/.dr.txt /tmp/spooldir/dr/dr.txt



Problem Scenario 60 : You have been given below code snippet.
val a = sc.parallelize(List("dog", "salmon", "salmon", "rat", "elephant"}, 3}
val b = a.keyBy(_.length)
val c = sc.parallelize(List("dog", "cat", "gnu", "salmon", "rabbit", "turkey", "woif", "bear", "bee"), 3)
val d = c.keyBy(_.length)
operation1
Write a correct code snippet for operationl which will produce desired output, shown below.
Array[(lnt, (String, String))] = Array((6, (salmon, salmon)), (6, (salmon, rabbit)), (6, (salmon, turkey)), (6, (salmon, salmon)), (6, (salmon, rabbit)),
(6, (salmon, turkey)), (3, (dog, dog)), (3, (dog, cat)), (3, (dog, gnu)), (3, (dog, bee)), (3, (rat, dog)), (3, (rat, cat)), (3, (rat, gnu)), (3, (rat, bee)))

  1. See the explanation for Step by Step Solution and configuration.

Answer(s): A

Explanation:

solution:
b.join(d).collect
join [Pair]: Performs an inner join using two key-value RDDs. Please note that the keys must be generally comparable to make this work. keyBy : Constructs two-component tuples (key-value pairs) by applying a function on each data item. The result of the function becomes the data item becomes the key and the original value of the newly created tuples.



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.

  1. See the explanation for Step by Step Solution and configuration.

Answer(s): A

Explanation:

Solution :

Step 1:
hdfs dfs -mkdir sparksql4
hdfs dfs -put course.txt sparksql4/
hdfs dfs -put fee.txt sparksql4/
Step 2: Now in spark shell
// load the data into a new RDD
val course = sc.textFile("sparksql4/course.txt")
val fee = sc.textFile("sparksql4/fee.txt")
// Return the first element in this RDD
course.fi rst()
fee.fi rst()
//define the schema using a case class case class Course(id: Integer, name: String) case class Fee(id: Integer, fee: Integer)
// create an RDD of Product objects
val courseRDD = course.map(_.split(", ")).map(c => Course(c(0).tolnt, c(1))) val feeRDD =fee.map(_.split(", ")).map(c => Fee(c(0}.tolnt, c(1}.tolnt)) courseRDD.first()
courseRDD.count(}
feeRDD.first()
feeRDD.countQ
// change RDD of Product objects to a DataFrame val courseDF = courseRDD.toDF(} val feeDF = feeRDD.toDF{)
// register the DataFrame as a temp table courseDF. registerTempTable("course") feeDF.
registerTempTablef'fee")
// Select data from table
val results = sqlContext.sql(......SELECT' FROM course """ ) results. showQ
val results = sqlContext.sql(......SELECT' FROM fee......) results. showQ
val results = sqlContext.sql(......SELECT * FROM course LEFT JOIN fee ON course.id = fee.id......)
results-showQ
val results ="sqlContext.sql(......SELECT * FROM course RIGHT JOIN fee ON course.id = fee.id "MM )
results. showQ
val results = sqlContext.sql(......SELECT' FROM course LEFT JOIN fee ON course.id = fee.id where fee.id IS NULL"
results. show()






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