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NEW QUESTION: 1
CORRECT TEXT
Problem Scenario 32 : You have given three files as below.
spark3/sparkdir1/file1.txt
spark3/sparkd ir2ffile2.txt
spark3/sparkd ir3Zfile3.txt
Each file contain some text.
spark3/sparkdir1/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 spark3/sparkdir2/file2.txt
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.
spark3/sparkdir3/file3.txt
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
Now write a Spark code in scala which will load all these three files from hdfs and do the word count by filtering following words. And result should be sorted by word count in reverse order.
Filter words ("a","the","an", "as", "a","with","this","these","is","are","in", "for",
"to","and","The","of")
Also please make sure you load all three files as a Single RDD (All three files must be loaded using single API call).
You have also been given following codec
import org.apache.hadoop.io.compress.GzipCodec
Please use above codec to compress file, while saving in hdfs.
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 content from all files.
val content =
sc.textFile("spark3/sparkdir1/file1.txt,spark3/sparkdir2/file2.txt,spark3/sparkdir3/file3.
txt") //Load the text file
Step 3 : Now create split each line and create RDD of words.
val flatContent = content.flatMap(word=>word.split(" "))
step 4 : Remove space after each word (trim it)
val trimmedContent = f1atContent.map(word=>word.trim)
Step 5 : Create an RDD from remove, all the words that needs to be removed.
val removeRDD = sc.parallelize(List("a","theM,ManM, "as",
"a","with","this","these","is","are'\"in'\ "for", "to","and","The","of"))
Step 6 : Filter the RDD, so it can have only content which are not present in removeRDD.
val filtered = trimmedContent.subtract(removeRDD}
Step 7 : Create a PairRDD, so we can have (word,1) tuple or PairRDD. val pairRDD = filtered.map(word => (word,1))
Step 8 : Now do the word count on PairRDD. val wordCount = pairRDD.reduceByKey(_ +
_)
Step 9 : Now swap PairRDD.
val swapped = wordCount.map(item => item.swap)
Step 10 : Now revers order the content. val sortedOutput = swapped.sortByKey(false)
Step 11 : Save the output as a Text file. sortedOutput.saveAsTextFile("spark3/result")
Step 12 : Save compressed output.
import org.apache.hadoop.io.compress.GzipCodec
sortedOutput.saveAsTextFile("spark3/compressedresult", classOf[GzipCodec])

NEW QUESTION: 2

A. antispam
B. anti-Dos
C. reputation filter
D. DLP
E. content filter
Answer: A,E

NEW QUESTION: 3
必要に応じて、次のログイン資格情報を使用します。
Azureユーザー名：xxxxx
Azureパスワード：xxxxx
次の情報は、テクニカルサポートのみを目的としています。
ラボインスタンス：10543936

米国西部地域にあるdb1-copy10543936という名前の新しいAzure SQLサーバーにdb1を複製する必要があります。
このタスクを完了するには、Azureポータルにサインインします。
Answer:
Explanation:
See the explanation below.
Explanation
1. In the Azure portal, browse to the database db1-copy10543936 that you want to set up for geo-replication.
2. On the SQL database page, select geo-replication, and then select the region to create the secondary database: US West region

3. Select or configure the server and pricing tier for the secondary database.

4. Click Create to add the secondary.
5. The secondary database is created and the seeding process begins.

6. When the seeding process is complete, the secondary database displays its status.

Reference:
https://docs.microsoft.com/en-us/azure/sql-database/sql-database-active-geo-replication-portal

NEW QUESTION: 4
The CEO of a large enterprise has announced me commencement of a major business expansion that will double the size of the organization. IT will need to support the expected demand expansion. What should the CIO do FIRST?
A. Recruit IT resources based on the expansion decision.
B. Embed IT personnel in the business units.
C. Review the resource utilisation matrix.
D. Update the IT strategic plan to align with the decision.
Answer: D