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NEW QUESTION: 1
You are designing an Azure Data Factory pipeline for processing data. The pipeline will process data that is stored in general-purpose standard Azure storage.
You need to ensure that the compute environment is created on-demand and removed when the process is completed.
Which type of activity should you recommend?
A. Data Lake Analytics U-SQL activity
B. HDInsight Pig activity
C. Databricks Python activity
D. Databricks Jar activity
Answer: B
Explanation:
Explanation
The HDInsight Pig activity in a Data Factory pipeline executes Pig queries on your own or on-demand HDInsight cluster.
References:
https://docs.microsoft.com/en-us/azure/data-factory/transform-data-using-hadoop-pig

NEW QUESTION: 2
A BPM Application developer is creating a client-side human service. The developer must create a save button that saves the coach fields so the user can return to their task at a later date.
Which service meets the requirements?
A:

B:

C:

D:

A. Option B
B. Option A
C. Option C
D. Option D
Answer: B

NEW QUESTION: 3
ユニバーサルコンテナは、M＆Aチームが潜在的な新しいプロジェクトで協力するために、おしゃべりグループを使用したいと考えています。このグループは、非メンバーが表示または参加できるように表示されるべきではなく、招待によってのみアクセスできます。 App Builderはどのチャターグループタイプを推奨する必要がありますか？
A. プライベートグループ
B. パブリックグループ
C. 非公開グループ
D. メンバーグループ
Answer: C

NEW QUESTION: 4
CORRECT TEXT
Problem Scenario 33 : You have given a files as below.
spark5/EmployeeName.csv (id,name)
spark5/EmployeeSalary.csv (id,salary)
Data is given below:
EmployeeName.csv
E01,Lokesh
E02,Bhupesh
E03,Amit
E04,Ratan
E05,Dinesh
E06,Pavan
E07,Tejas
E08,Sheela
E09,Kumar
E10,Venkat
EmployeeSalary.csv
E01,50000
E02,50000
E03,45000
E04,45000
E05,50000
E06,45000
E07,50000
E08,10000
E09,10000
E10,10000
Now write a Spark code in scala which will load these two tiles from hdfs and join the same, and produce the (name.salary) values.
And save the data in multiple tile group by salary (Means each file will have name of employees with same salary). Make sure file name include salary as well.
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 EmployeeName.csv file from hdfs and create PairRDDs
val name = sc.textFile("spark5/EmployeeName.csv")
val namePairRDD = name.map(x=> (x.split(",")(0),x.split('V')(1)))
Step 3 : Load EmployeeSalary.csv file from hdfs and create PairRDDs
val salary = sc.textFile("spark5/EmployeeSalary.csv")
val salaryPairRDD = salary.map(x=> (x.split(",")(0),x.split(",")(1)))
Step 4 : Join all pairRDDS
val joined = namePairRDD.join(salaryPairRDD}
Step 5 : Remove key from RDD and Salary as a Key. val keyRemoved = joined.values
Step 6 : Now swap filtered RDD.
val swapped = keyRemoved.map(item => item.swap)
Step 7 : Now groupBy keys (It will generate key and value array) val grpByKey = swapped.groupByKey().collect()
Step 8 : Now create RDD for values collection
val rddByKey = grpByKey.map{case (k,v) => k->sc.makeRDD(v.toSeq)}
Step 9 : Save the output as a Text file.
rddByKey.foreach{ case (k,rdd) => rdd.saveAsTextFile("spark5/Employee"+k)}