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NEW QUESTION: 1
You are establishing a ClusterXL environment, with the following topology:
VIP internal cluster IP = 172.16.10.3; VIP external cluster IP = 192.168.10.3
Cluster Member 1: 4 NICs, 3 enableD. hme0: 192.168.10.1/24, hme1: 10.10.10.1/24, qfe2: 172.16.10.1/24
Cluster Member 2: 5 NICs, 3 enabled; hme3: 192.168.10.2/24, hme1: 10.10.10.2/24, hme2: 172.16.10.2/24
External interfaces 192.168.10.1 and 192.168.10.2 connect to a VLAN switch. The upstream router connects to the same VLAN switch. Internal interfaces 172.16.10.1 and 172.16.10.2 connect to a hub. 10.10.10.0 is the synchronization network. The Security Management Server is located on the internal network with IP 172.16.10.3. What is the problem with this configuration?
A. The Security Management Server must be in the dedicated synchronization network, not the internal network.
B. The Cluster interface names must be identical across all cluster members.
C. Cluster members cannot use the VLAN switch. They must use hubs.
D. There is an IP address conflict.
Answer: D

NEW QUESTION: 2
Quality award given in Japan is:
A. Juran Prize
B. Japanese Quality Award
C. Deming Prize
D. Malcolm Baldrige National Quality Awards
Answer: C

NEW QUESTION: 3
A system administrator needs to create a deployment manager and a federated node in one iteration
through the Profile Management Tool.
Which profile type should the system administrator select?
A. Custom profile
B. Management profile with a deployment manager server
C. Management profile with a job manager server
D. Cell profile
Answer: D

NEW QUESTION: 4
CORRECT TEXT
Problem Scenario 33 : You have given a files as below.
spark5/EmployeeName.csv (id,name)
spark5/EmployeeSalary.csv (id,salary)
Data is given below:
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
Now write a Spark code in scala which will load these two tiles from hdfs and join the same, and produce the (name.salary) values.
And save the data in multiple tile group by salary (Means each file will have name of employees with same salary). Make sure file name include salary as well.
Answer:
Explanation:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Create all three files in hdfs (We will do using Hue). However, you can first create in local filesystem and then upload it to hdfs.
Step 2 : Load EmployeeName.csv file from hdfs and create PairRDDs
val name = sc.textFile("spark5/EmployeeName.csv")
val namePairRDD = name.map(x=> (x.split(",")(0),x.split('V')(1)))
Step 3 : Load EmployeeSalary.csv file from hdfs and create PairRDDs
val salary = sc.textFile("spark5/EmployeeSalary.csv")
val salaryPairRDD = salary.map(x=> (x.split(",")(0),x.split(",")(1)))
Step 4 : Join all pairRDDS
val joined = namePairRDD.join(salaryPairRDD}
Step 5 : Remove key from RDD and Salary as a Key. val keyRemoved = joined.values
Step 6 : Now swap filtered RDD.
val swapped = keyRemoved.map(item => item.swap)
Step 7 : Now groupBy keys (It will generate key and value array) val grpByKey = swapped.groupByKey().collect()
Step 8 : Now create RDD for values collection
val rddByKey = grpByKey.map{case (k,v) => k->sc.makeRDD(v.toSeq)}
Step 9 : Save the output as a Text file.
rddByKey.foreach{ case (k,rdd) => rdd.saveAsTextFile("spark5/Employee"+k)}