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NEW QUESTION: 1
Which of the following tools is spyware that makes Windows clients send their passwords as clear text?
A. C2MYAZZ
B. KrbCrack
C. Pwddump2
D. SMBRelay
Answer: A

NEW QUESTION: 2
温度センサーから測定データを生成するAzure IoT Edgeデバイスがあります。データの変化が非常に遅い。
一時的な2分のウィンドウでデータを分析する必要があります。温度が制限を5度上回った場合は、アラートを生成する必要があります。このソリューションでは、カスタムコードの開発を最小限に抑える必要があります。
何を使うべきですか？
A. Azure Stream Analytics as an IoT Edge module
B. Azure Functions as an IoT Edge module
C. A Machine Learning model as a web service
D. an Azure Machine Learning model as an IoT Edge module
Answer: A

NEW QUESTION: 3
CORRECT TEXT
Problem Scenario 31 : You have given following two files
1 . Content.txt: Contain a huge text file containing space separated words.
2 . Remove.txt: Ignore/filter all the words given in this file (Comma Separated).
Write a Spark program which reads the Content.txt file and load as an RDD, remove all the words from a broadcast variables (which is loaded as an RDD of words from Remove.txt).
And count the occurrence of the each word and save it as a text file in HDFS.
Content.txt
Hello this is ABCTech.com
This is TechABY.com
Apache Spark Training
This is Spark Learning Session
Spark is faster than MapReduce
Remove.txt
Hello, is, this, the
Answer:
Explanation:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Create all three files in hdfs in directory called spark2 (We will do using Hue).
However, you can first create in local filesystem and then upload it to hdfs
Step 2 : Load the Content.txt file
val content = sc.textFile("spark2/Content.txt") //Load the text file
Step 3 : Load the Remove.txt file
val remove = sc.textFile("spark2/Remove.txt") //Load the text file
Step 4 : Create an RDD from remove, However, there is a possibility each word could have trailing spaces, remove those whitespaces as well. We have used two functions here flatMap, map and trim.
val removeRDD= remove.flatMap(x=> x.splitf',") ).map(word=>word.trim)//Create an array of words
Step 5 : Broadcast the variable, which you want to ignore
val bRemove = sc.broadcast(removeRDD.collect().toList) // It should be array of Strings
Step 6 : Split the content RDD, so we can have Array of String. val words = content.flatMap(line => line.split(" "))
Step 7 : Filter the RDD, so it can have only content which are not present in "Broadcast
Variable". val filtered = words.filter{case (word) => !bRemove.value.contains(word)}
Step 8 : Create a PairRDD, so we can have (word,1) tuple or PairRDD. val pairRDD = filtered.map(word => (word,1))
Step 9 : Nowdo the word count on PairRDD. val wordCount = pairRDD.reduceByKey(_ + _)
Step 10 : Save the output as a Text file.
wordCount.saveAsTextFile("spark2/result.txt")

NEW QUESTION: 4
DFG is the largest bridge-building company in its home country, H.
DFG works exclusively for the government of country H and the government awards DFG 80% of the contracts to build new bridges.
DFG's directors are considering using the big data approach to identify opportunities to increase sales revenues and profit.
Which of the following statements are true?
A. DFG should supplement its existing databases in order to enhance the benefit from big data.
B. Big data has the potential to identify ways in which DFG can reduce construction costs.
C. It would be unethical for DFG to use big data in order to compete even more effectively with its rivals.
D. Big data has the potential to identify ways in which DFG can increase sales.
E. The big data approach may help DFG to price contract bids more accurately.
Answer: A,B,E