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NEW QUESTION: 1
Your company has an Active Directory forest that contains 250 domains. All domain controllers run Windows Server 2003. The forest functional level is Windows 2000. The domain functional level is Windows 2000 mixed.
You are planning to migrate the domain controllers in only one domain to Windows Server 2008 R2.
You need to ensure that Kerberos can be encrypted with Advanced Encryption Standard (AES) after the migration.
What should you recommend? (More than one answer choice may achieve the goal. Select the BEST answer).
A. Raise the forest functional level to Windows Server 2003.
B. Raise the domain functional level to Windows Server 2003.
C. Raise the forest functional level to Windows Server 2008 R2.
D. Raise the domain functional level to Windows Server 2008.
Answer: D
Explanation:
domain + 2008
Kerberos with AES is an enhancement of Server 2008.
see:
http://technet.microsoft.com/en-us/library/cc749438(v=ws.10).aspx
This Windows Vista and Windows Server 2008 security enhancement enables the use of AES 128 and
AES 256 encryption with the Kerberos authentication protocol. This enhancement includes the following
changes from Windows XP:
AES support for the base Kerberos authentication protocol. The base Kerberos protocol in Windows Vista supports AES for encryption of ticket-granting tickets (TGTs), service tickets, and session keys.
AES support for the Generic Security Service (GSS)-Kerberos mechanism. In addition to enabling AES for the base protocol, GSS messages (which conduct client/server communications in Windows Vista) are protected with AES.
Domain functional level
Windows Server 2008
All default Active Directory features, all features from the Windows Server 2003 domain functional level, plus the following features:
Distributed File System Replication support for SYSVOL, which provides more robust and detailed replication of SYSVOL contents.
Advanced Encryption Services (AES 128 and 256) support for the Kerberos authentication protocol.
Last Interactive Logon Information, which displays the time of the last successful interactive logon for a user, from what workstation, and the number of failed logon attempts since the last logon.
Fine-grained password policies, which make it possible for password policies and account lockout policies to be specified for users and global security groups in a domain.

NEW QUESTION: 2
ビジネスクリティカルなMySqlデータベースがAmazon EC2インスタンスで実行されています。保管性能と耐久性はアプリケーションにとって重要です。
どのボリュームタイプが、1桁のミリ秒の待ち時間と持続的なIOPSパフォーマンスを備えた永続的なボリュームを提供しますか？
A. Amazon EBS汎用SSD
B. Amazon EBSコールドHDD
C. Amazon EBSプロビジョンドIOPS SSD
D. Amazon EC2インスタンスストア
Answer: D

NEW QUESTION: 3
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)}