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NEW QUESTION: 1
Consider the following diagram from an object studio action:

What is the problem with this flow?
A. There should be a wait stage at the end of the flow, before the end stage to ensure the flow logic has worked correctly.
B. There is a risk of the flow causing an infinite loop.
C. The Exception Block should be removed from the page so that all exceptions on that page can be recovered.
D. There is nothing wrong with the object flow shown in this image.
Answer: C

NEW QUESTION: 2
A technician has racked a new access switch and has run multimode fiber to a new location. After
installing an extended-range 10Gb SFP in the core switch, the technician installed a 10Gb SFP in the
access switch and connected the port to the new extension with a fiber jumper. However, the link does not
display, and the technician cannot see light emitting from the core switch. Which of the following solutions
is MOST likely to resolve the problem?
A. Swap the fiber pairs in one of the SFPs
B. Replace the jumpers with single-mode fiber
C. Set the correct MTU on the new interface
D. Install an optic that matches the fiber type
Answer: B

NEW QUESTION: 3
製造組織は、同様の会社との合併を検討しており、最高監査責任者（CAE）にデューデリジェンス監査を実施するよう要求しています。予備調査中に、CAEは、在庫管理がリスクの高い領域であると指摘しています。 CAEは、外部監査人および法律顧問と協議して、それらがそれらの懸念を共有していることを知ります。次のうち、CAEの最善の行動方針はどれですか？
A. 合併会社の在庫管理慣行の独立監査を実施して、懸念事項を検証し、検討と行動のために経営陣に関連性のある信頼できる結果を提供します。
B. 内部監査、外部監査、および法律顧問はすべて在庫管理について懸念を持っており、在庫の重要性が高いことを考えると、経営陣は合併を進めるべきではないことを経営陣に助言します。
C. 在庫管理のレビューを外部監査人および法律顧問と調整し、各グループが専門分野に焦点を合わせて、問題の範囲を確認します。
D. 合併会社の内部監査部門と調整して、在庫管理機能と懸念事項が十分に根拠があるかどうかをよりよく理解します。
Answer: C

NEW QUESTION: 4
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])