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NEW QUESTION: 1
InfoSphere Data Architect is not part of the Information Server Suite. So, can metadata from this tool be brought into the Metadata Repository?
A. No, metadata cannot be imported into the Metadata Repository from InfoSphere Data Architect.
B. Only a physical model can be exported from Data Architect to the Metadata Server.
C. Only a glossary model can be exported from Data Architect to the Metadata Server.
D. Both a physical model and a glossary model can be exported from Data Architect to the Metadata Server.
Answer: D
NEW QUESTION: 2
Refer to the exhibit.
The exhibit shows how two NICs on a physical server connect to two HP 5820 switches. The server supports eight virtual machines (VMs) with VMware version 5.1. The VMware standard virtual switch is bound to NIC and NIC2. This switch implements source MAC load balancing for the NIC team.
What is the proper configuration for ports 1/0/1 and 2/0/1?
A. Do not place the ports in a bridge aggregation group
B. Place the ports in a bridge aggregation group that does not use LACP
C. Place the ports in a bridge aggregation group that uses LACP
D. Enable LACP on the individual ports
Answer: D
NEW QUESTION: 3
CORRECT TEXT
Problem Scenario 34 : You have given a file named spark6/user.csv.
Data is given below:
user.csv
id,topic,hits
Rahul,scala,120
Nikita,spark,80
Mithun,spark,1
myself,cca175,180
Now write a Spark code in scala which will remove the header part and create RDD of values as below, for all rows. And also if id is myself" than filter out row.
Map(id -> om, topic -> scala, hits -> 120)
Answer:
Explanation:
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 user.csv file from hdfs and create PairRDDs val csv =
sc.textFile("spark6/user.csv")
Step 3 : split and clean data
val headerAndRows = csv.map(line => line.split(",").map(_.trim))
Step 4 : Get header row
val header = headerAndRows.first
Step 5 : Filter out header (We need to check if the first val matches the first header name) val data = headerAndRows.filter(_(0) != header(O))
Step 6 : Splits to map (header/value pairs)
val maps = data.map(splits => header.zip(splits).toMap)
step 7: Filter out the user "myself
val result = maps.filter(map => mapf'id") != "myself")
Step 8 : Save the output as a Text file. result.saveAsTextFile("spark6/result.txt")
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