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NEW QUESTION: 1
Windows 10を実行し、Active Directoryドメインに参加している200台のコンピューターがあります。
グループポリシーを使用して、すべてのコンピューターでWindowsリモート管理（WinRM）を有効にする必要があります。
どの3つのアクションを実行する必要がありますか？それぞれの正解は、ソリューションの一部を示しています。
注：それぞれの正しい選択は1ポイントの価値があります。
A. Windowsファイアウォールを有効にします。受信リモート管理の例外設定を許可します。
B. リモートシェルアクセスを許可する設定を有効にします。
C. リモートレジストリサービスのスタートアップの種類を自動に設定します。
D. Windowsリモート管理（WS-Management）サービスのスタートアップの種類を自動に設定します。
E. WinRM設定によるリモートサーバー管理を許可するを有効にします。
F. Windowsファイアウォールを有効にします：受信リモートデスクトップの例外設定を許可します。
Answer: A,D,E
Explanation:
Explanation
References:
https://support.auvik.com/hc/en-us/articles/204424994-How-to-enable-WinRM-with-domain-controller-Group-

NEW QUESTION: 2
According to Marshall, ______ are probable future economic benefits obtained or controlled by a particular entity as a result of past transactions or events.
A. Assets
B. Liabilities
C. None of above
D. Credentials
Answer: A

NEW QUESTION: 3
CORRECT TEXT
Problem Scenario 68 : You have given a file as below.
spark75/f ile1.txt
File contain some text. As given Below
spark75/file1.txt
Apache Hadoop is an open-source software framework written in Java for distributed storage and distributed processing of very large data sets on computer clusters built from commodity hardware. All the modules in Hadoop are designed with a fundamental assumption that hardware failures are common and should be automatically handled by the framework
The core of Apache Hadoop consists of a storage part known as Hadoop Distributed File
System (HDFS) and a processing part called MapReduce. Hadoop splits files into large blocks and distributes them across nodes in a cluster. To process data, Hadoop transfers packaged code for nodes to process in parallel based on the data that needs to be processed.
his approach takes advantage of data locality nodes manipulating the data they have access to to allow the dataset to be processed faster and more efficiently than it would be in a more conventional supercomputer architecture that relies on a parallel file system where computation and data are distributed via high-speed networking
For a slightly more complicated task, lets look into splitting up sentences from our documents into word bigrams. A bigram is pair of successive tokens in some sequence.
We will look at building bigrams from the sequences of words in each sentence, and then try to find the most frequently occuring ones.
The first problem is that values in each partition of our initial RDD describe lines from the file rather than sentences. Sentences may be split over multiple lines. The glom() RDD method is used to create a single entry for each document containing the list of all lines, we can then join the lines up, then resplit them into sentences using "." as the separator, using flatMap so that every object in our RDD is now a sentence.
A bigram is pair of successive tokens in some sequence. Please build bigrams from the sequences of words in each sentence, and then try to find the most frequently occuring ones.
Answer:
Explanation:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Create all three tiles in hdfs (We will do using Hue}. However, you can first create in local filesystem and then upload it to hdfs.
Step 2 : The first problem is that values in each partition of our initial RDD describe lines from the file rather than sentences. Sentences may be split over multiple lines.
The glom() RDD method is used to create a single entry for each document containing the list of all lines, we can then join the lines up, then resplit them into sentences using "." as the separator, using flatMap so that every object in our RDD is now a sentence.
sentences = sc.textFile("spark75/file1.txt") \ .glom() \
map(lambda x: " ".join(x)) \ .flatMap(lambda x: x.spllt("."))
Step 3 : Now we have isolated each sentence we can split it into a list of words and extract the word bigrams from it. Our new RDD contains tuples containing the word bigram (itself a tuple containing the first and second word) as the first value and the number 1 as the second value. bigrams = sentences.map(lambda x:x.split())
\ .flatMap(lambda x: [((x[i],x[i+1]),1)for i in range(0,len(x)-1)])
Step 4 : Finally we can apply the same reduceByKey and sort steps that we used in the wordcount example, to count up the bigrams and sort them in order of descending frequency. In reduceByKey the key is not an individual word but a bigram.
freq_bigrams = bigrams.reduceByKey(lambda x,y:x+y)\
map(lambda x:(x[1],x[0])) \
sortByKey(False)
freq_bigrams.take(10)