CCA175 Dumps CCA175 Braindumps CCA175 Real Questions CCA175 Practice Test CCA175 Actual Questions Cloudera CCA175 CCA Spark and Hadoop Developer https://killexams.com/pass4sure/exam-detail/CCA175 Question: 94 Now import the data from following directory into departments_export table, /user/cloudera/departments new Answer: Solution: Step 1: Login to musql db mysql –user=retail_dba -password=cloudera show databases; use retail_db; show tables; step 2: Create a table as given in problem statement. CREATE table departments_export (departmentjd int(11), department_name varchar(45), created_date T1MESTAMP DEFAULT NOW()); show tables; Step 3: Export data from /user/cloudera/departmentsnew to new table departments_export sqoop export -connect jdbc:mysql://quickstart:3306/retail_db -username retaildba –password cloudera –table departments_export -export-dir /user/cloudera/departments_new -batch Step 4: Now check the export is correctly done or not. mysql -user*retail_dba -password=cloudera show databases; use retail _db; show tables; select’ from departments_export; Question: 95 Data should be written as text to hdfs Answer: Solution: Step 1: Create directory mkdir /tmp/spooldir2 Step 2: Create flume configuration file, with below configuration for source, sink and channel and save it in flume8.conf. agent1 .sources = source1 agent1.sinks = sink1a sink1b agent1.channels = channel1a channel1b agent1.sources.source1.channels = channel1a channel1b agent1.sources.source1.selector.type = replicating agent1.sources.source1.selector.optional = channel1b agent1.sinks.sink1a.channel = channel1a agent1 .sinks.sink1b.channel = channel1b agent1.sources.source1.type = spooldir agent1 .sources.sourcel.spoolDir = /tmp/spooldir2 agent1.sinks.sink1a.type = hdfs agent1 .sinks, sink1a.hdfs. path = /tmp/flume/primary agent1 .sinks.sink1a.hdfs.tilePrefix = events agent1 .sinks.sink1a.hdfs.fileSuffix = .log agent1 .sinks.sink1a.hdfs.fileType = Data Stream agent1 . sinks.sink1b.type = hdfs agent1 . sinks.sink1b.hdfs.path = /tmp/flume/secondary agent1 .sinks.sink1b.hdfs.filePrefix = events agent1.sinks.sink1b.hdfs.fileSuffix = .log agent1 .sinks.sink1b.hdfs.fileType = Data Stream agent1.channels.channel1a.type = file agent1.channels.channel1b.type = memory 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/flumeconf/flume8.conf –name age Step 5: Open another terminal and create a file in /tmp/spooldir2/ echo "IBM, 100, 20160104" » /tmp/spooldir2/.bb.txt echo "IBM, 103, 20160105" » /tmp/spooldir2/.bb.txt mv /tmp/spooldir2/.bb.txt /tmp/spooldir2/bb.txt After few mins echo "IBM.100.2, 20160104" »/tmp/spooldir2/.dr.txt echo "IBM, 103.1, 20160105" » /tmp/spooldir2/.dr.txt mv /tmp/spooldir2/.dr.txt /tmp/spooldir2/dr.txt Question: 96 Data should be written as text to hdfs Answer: 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 Question: 97 Data should be written as text to hdfs Answer: Solution: Step 1: Create directory mkdir /tmp/spooldir2 Step 2: Create flume configuration file, with below configuration for source, sink and channel and save it in flume8.conf. agent1 .sources = source1 agent1.sinks = sink1a sink1b agent1.channels = channel1a channel1b agent1.sources.source1.channels = channel1a channel1b agent1.sources.source1.selector.type = replicating agent1.sources.source1.selector.optional = channel1b agent1.sinks.sink1a.channel = channel1a agent1 .sinks.sink1b.channel = channel1b agent1.sources.source1.type = spooldir agent1 .sources.sourcel.spoolDir = /tmp/spooldir2 agent1.sinks.sink1a.type = hdfs agent1 .sinks, sink1a.hdfs. path = /tmp/flume/primary agent1 .sinks.sink1a.hdfs.tilePrefix = events agent1 .sinks.sink1a.hdfs.fileSuffix = .log agent1 .sinks.sink1a.hdfs.fileType = Data Stream agent1 . sinks.sink1b.type = hdfs agent1 . sinks.sink1b.hdfs.path = /tmp/flume/secondary agent1 .sinks.sink1b.hdfs.filePrefix = events agent1.sinks.sink1b.hdfs.fileSuffix = .log agent1 .sinks.sink1b.hdfs.fileType = Data Stream agent1.channels.channel1a.type = file agent1.channels.channel1b.type = memory 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/flumeconf/flume8.conf –name age Step 5: Open another terminal and create a file in /tmp/spooldir2/ echo "IBM, 100, 20160104" » /tmp/spooldir2/.bb.txt echo "IBM, 103, 20160105" » /tmp/spooldir2/.bb.txt mv /tmp/spooldir2/.bb.txt /tmp/spooldir2/bb.txt After few mins echo "IBM.100.2, 20160104" »/tmp/spooldir2/.dr.txt echo "IBM, 103.1, 20160105" » /tmp/spooldir2/.dr.txt mv /tmp/spooldir2/.dr.txt /tmp/spooldir2/dr.txt Question: 98 Data should be written as text to hdfs Answer: 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 ABCTech m.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 Question: 99 Problem Scenario 4: You have been given MySQL DB with following details. user=retail_dba password=cloudera database=retail_db table=retail_db.categories jdbc URL = jdbc:mysql://quickstart:3306/retail_db Please accomplish following activities. Import Single table categories (Subset data} to hive managed table, where category_id between 1 and 22 Answer: Solution: Step 1: Import Single table (Subset data) sqoop import –connect jdbc:mysql://quickstart:3306/retail_db -username=retail_dba -password=cloudera - table=categories -where " ’ category_id ’ between 1 and 22" –hive-import –m 1 Note: Here the ‘ is the same you find on ~ key This command will create a managed table and content will be created in the following directory. /user/hive/warehouse/categories Step 2: Check whether table is created or not (In Hive) show tables; select * from categories; Question: 100 Data should be written as text to hdfs Answer: 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 Question: 101 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 Answer: 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 Question: 102 Problem Scenario 23: You have been given log generating service as below. Start_logs (It will generate continuous logs) Tail_logs (You can check, what logs are being generated) Stop_logs (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 flume3.conf, using that configuration file dumps logs in HDFS file system in a directory called flumeflume3/%Y/%m/%d/%H/%M Means every minute new directory should be created). Please us the interceptors to provide timestamp information, if message header does not have header info. And also note that you have to preserve existing timestamp, if message contains it. 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. Answer: 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 agentl.sources.source1.command = tail -F /opt/gen logs/logs/access.log #Define interceptors agent1 .sources.source1.interceptors=i1 agent1 .sources.source1.interceptors.i1.type=timestamp agent1 .sources.source1.interceptors.i1.preserveExisting=true ## Describe sink1 agent1 .sinks.sink1.channel = memory-channel agent1 . sinks.sink1.type = hdfs agent1 . sinks.sink1.hdfs.path = flume3/%Y/%m/%d/%H/%M agent1 .sinks.sjnkl.hdfs.fileType = Data Stream # Now we need to define channel1 property. agent1.channels.channel1.type = memory agent1.channels.channel1.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 2: Run below command which will use this configuration file and append data in hdfs. Start log service using: start_logs Start flume service: flume-ng agent -conf /home/cloudera/flumeconf -conf-file /home/cloudera/flumeconf/flume3.conf - DfIume.root.logger=DEBUG, INFO, console Cname agent1 Wait for few mins and than stop log service. stop logs Question: 103 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 Answer: 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 Question: 104 Now import data from mysql table departments to this hive table. Please make sure that data should be visible using below hive command, select" from departments_hive Answer: Solution: Step 1: Create hive table as said. hive show tables; create table departments_hive(department_id int, department_name string); Step 2: The important here is, when we create a table without delimiter fields. Then default delimiter for hive is ^A (01). Hence, while importing data we have to provide proper delimiter. sqoop import -connect jdbc:mysql://quickstart:3306/retail_db ~username=retail_dba -password=cloudera –table departments –hive-home /user/hive/warehouse -hive-import -hive-overwrite –hive-table departments_hive –fields-terminated-by ‘01’ Step 3: Check-the data in directory. hdfs dfs -Is /user/hive/warehouse/departments_hive hdfs dfs -cat/user/hive/warehouse/departmentshive/part’ Check data in hive table. Select * from departments_hive; Question: 105 Import departments table as a text file in /user/cloudera/departments. Answer: Solution: Step 1: List tables using sqoop sqoop list-tables –connect jdbc:mysql://quickstart:330G/retail_db –username retail dba -password cloudera Step 2: Eval command, just run a count query on one of the table. sqoop eval –connect jdbc:mysql://quickstart:3306/retail_db -username retail_dba -password cloudera –query "select count(1) from ordeMtems" Step 3: Import all the tables as avro file. sqoop import-all-tables -connect jdbc:mysql://quickstart:3306/retail_db -username=retail_dba -password=cloudera -as-avrodatafile -warehouse-dir=/user/hive/warehouse/retail stage.db -ml Step 4: Import departments table as a text file in /user/cloudera/departments sqoop import -connect jdbc:mysql://quickstart:3306/retail_db -username=retail_dba -password=cloudera -table departments -as-textfile -target-dir=/user/cloudera/departments Step 5: Verify the imported data. hdfs dfs -Is /user/cloudera/departments hdfs dfs -Is /user/hive/warehouse/retailstage.db hdfs dfs -Is /user/hive/warehouse/retail_stage.db/products Question: 106 Problem Scenario 2: There is a parent organization called "ABC Group Inc", which has two child companies named Tech Inc and MPTech. Both companies employee information is given in two separate text file as below. Please do the following activity for employee details. Tech Inc.txt Answer: Solution: Step 1: Check All Available command hdfs dfs Step 2: Get help on Individual command hdfs dfs -help get Step 3: Create a directory in HDFS using named Employee and create a Dummy file in it called e.g. Techinc.txt hdfs dfs -mkdir Employee Now create an emplty file in Employee directory using Hue. Step 4: Create a directory on Local file System and then Create two files, with the given data in problems. Step 5: Now we have an existing directory with content in it, now using HDFS command line, overrid this existing Employee directory. While copying these files from local file System to HDFS. cd /home/cloudera/Desktop/ hdfs dfs - put -f Employee Step 6: Check All files in directory copied successfully hdfs dfs -Is Employee Step 7: Now merge all the files in Employee directory, hdfs dfs -getmerge -nl Employee MergedEmployee.txt Step 8: Check the content of the file. cat MergedEmployee.txt Step 9: Copy merged file in Employeed directory from local file ssytem to HDFS. hdfs dfs -put MergedEmployee.txt Employee/ Step 10: Check file copied or not. hdfs dfs -Is Employee Step 11: Change the permission of the merged file on HDFS hdfs dfs -chmpd 664 Employee/MergedEmployee.txt Step 12: Get the file from HDFS to local file system, hdfs dfs -get Employee Employee_hdfs Question: 107 Problem Scenario 30: You have been given three csv files in hdfs as below. EmployeeName.csv with the field (id, name) EmployeeManager.csv (id, manager Name) EmployeeSalary.csv (id, Salary) Using Spark and its API you have to generate a joined output as below and save as a text tile (Separated by comma) for final distribution and output must be sorted by id. ld, name, salary, managerName EmployeeManager.csv E01, Vishnu E02, Satyam E03, Shiv E04, Sundar E05, John E06, Pallavi E07, Tanvir E08, Shekhar E09, Vinod E10, Jitendra EmployeeName.csv E01, Lokesh E02, Bhupesh E03, Amit E04, Ratan E05, Dinesh E06, Pavan E07, Tejas E08, Sheela E09, Kumar E10, Venkat EmployeeSalary.csv E01, 50000 E02, 50000 E03, 45000 E04, 45000 E05, 50000 E06, 45000 E07, 50000 E08, 10000 E09, 10000 E10, 10000 Answer: Solution: Step 1: Create all three files in hdfs in directory called sparkl (We will do using Hue}. However, you can first create in local filesystem and then Step 2: Load EmployeeManager.csv file from hdfs and create PairRDDs val manager = sc.textFile("spark1/EmployeeManager.csv") val managerPairRDD = manager.map(x=> (x.split(", ")(0), x.split(", ")(1))) Step 3: Load EmployeeName.csv file from hdfs and create PairRDDs val name = sc.textFile("spark1/EmployeeName.csv") val namePairRDD = name.map(x=> (x.split(", ")(0), x.split(‘")(1))) Step 4: Load EmployeeSalary.csv file from hdfs and create PairRDDs val salary = sc.textFile("spark1/EmployeeSalary.csv") val salaryPairRDD = salary.map(x=> (x.split(", ")(0), x.split(", ")(1))) Step 4: Join all pairRDDS val joined = namePairRDD.join(salaryPairRDD}.join(managerPairRDD} Step 5: Now sort the joined results, val joinedData = joined.sortByKey() Step 6: Now generate comma separated data. val finalData = joinedData.map(v=> (v._1, v._2._1._1, v._2._1._2, v._2._2)) Step 7: Save this output in hdfs as text file. finalData.saveAsTextFile("spark1/result.txt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