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NEW QUESTION: 1
EBS 스냅 샷 생성이 시작되었지만 완료되지 않은 경우 EBS 볼륨 :
A. 스냅 샷이 진행되는 동안 사용할 수 있습니다.
B. 스냅 샷이 완료 될 때까지 사용할 수 없습니다.
C. 스냅 샷 진행 중에 읽기 전용 모드로 사용할 수 있습니다.
D. 스냅 샷이 완료 될 때까지 EC2 인스턴스에 분리하거나 연결할 수 없습니다.
Answer: A

NEW QUESTION: 2
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: 3
A Project Management Office (PMO) manages a number of aspects, including what?
A. Central coordinator of communication management across projects, and for the delivery of specific project objectives, organization opportunity assessment
B. Overall risk, overall opportunity and interdependencies among projects
C. Assigns project resources to best meet project objectives
D. Project scope, schedule, cost, and quality of the products of the work packages
Answer: B