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NEW QUESTION: 1
Gemäß den Empfehlungen der Financial Action Task Force 40 umfassen designierte nichtfinanzielle Unternehmen und Berufe
A. Hawala-Operatoren.
B. Gelddienstleistungsunternehmen.
C. Immobilienmakler.
D. Rohstoffhändler.
Answer: A

NEW QUESTION: 2
CORRECT TEXT
Problem Scenario 49 : You have been given below code snippet (do a sum of values by key}, with intermediate output.
val keysWithValuesList = Array("foo=A", "foo=A", "foo=A", "foo=A", "foo=B", "bar=C",
"bar=D", "bar=D")
val data = sc.parallelize(keysWithValuesl_ist}
//Create key value pairs
val kv = data.map(_.split("=")).map(v => (v(0), v(l))).cache()
val initialCount = 0;
val countByKey = kv.aggregateByKey(initialCount)(addToCounts, sumPartitionCounts)
Now define two functions (addToCounts, sumPartitionCounts) such, which will produce following results.
Output 1
countByKey.collect
res3: Array[(String, Int)] = Array((foo,5), (bar,3))
import scala.collection._
val initialSet = scala.collection.mutable.HashSet.empty[String]
val uniqueByKey = kv.aggregateByKey(initialSet)(addToSet, mergePartitionSets)
Now define two functions (addToSet, mergePartitionSets) such, which will produce following results.
Output 2:
uniqueByKey.collect
res4: Array[(String, scala.collection.mutable.HashSet[String])] = Array((foo,Set(B, A}},
(bar,Set(C, D}}}
Answer:
Explanation:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
val addToCounts = (n: Int, v: String) => n + 1
val sumPartitionCounts = (p1: Int, p2: Int} => p1 + p2
val addToSet = (s: mutable.HashSet[String], v: String) => s += v
val mergePartitionSets = (p1: mutable.HashSet[String], p2: mutable.HashSet[String]) => p1
+ += p2

NEW QUESTION: 3
Ein Unternehmen möchte einen Standort erstellen, der während des Empfangsprozesses bei der Inspektion verwendet wird.
Welche Art von Ort wird verwendet?
A. Ein HOLDING-Standort auf Site-Ebene.
B. Ein INSPECTION-Standort auf ORG-Ebene.
C. Ein Ort zum Löschen auf ORG-Ebene.
D. Ein EMPFANGENDER Ort auf SITE-Ebene.
Answer: A

NEW QUESTION: 4
Azure Machine Learning StudioからWeka環境に大きなデータセットを移動しています。
Weka環境用にデータをフォーマットする必要があります。
どのモジュールを使用する必要がありますか？
A. データセットに変換
B. SVMLightに変換
C. CSVに変換
D. ARFFに変換
Answer: D
Explanation:
Use the Convert to ARFF module in Azure Machine Learning Studio, to convert datasets and results in Azure Machine Learning to the attribute-relation file format used by the Weka toolset. This format is known as ARFF.
The ARFF data specification for Weka supports multiple machine learning tasks, including data preprocessing, classification, and feature selection. In this format, data is organized by entites and their attributes, and is contained in a single text file.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/convert-to-arff

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