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Huawei HCIP-Big Data Developer V2.0 Sample Questions:

1. existSpark, assuminglinesIs anDStreamobject,filterStatements can be filtered out80%data for the following two The correct statement is:
X: lines.filter(...).groupByKey(...)
Y: lines.groupByKey(...).filter(...)

A) XCompareYlower performance
B) XandYsame as performance
C) XCompareYhigher performance
D) UncertaintyXandYdifference in performance

2. existSparkmiddle,SparkSQLis an independent module that does not depend onSparkCoretstandtasQaon.eaR.
operation, etc.

A) False
B) True

3. FusionInsight HDin, aboutLoaderIs the job description correct?

A) LoderSubmit an assignment toYARAfter the job is executed, no other jobs can be submitted until the job is executed Industry.
B) LoaderIf the job fails to execute, garbage data will be generated, which needs to be cleared manually by the user.
C) LoaderSubmit the job toARNAfter execution, if at this timeLoaderIf the service is abnormal, this job executes line failed.
D) LoaderSubmit the job toYARAfter a small execution, if a certainMapperTask execution fails, can be automatically Try again.

4. There are the following business scenarios: User online log files have been stored inHDFSabove, the log file content format the formula is: each online record has three fields, namely name, gender, and online time, and the fields are separated by ","; It is required to print out all female netizens who spend more than two hours online. Which of the following code snippets can achieve The above business scenario? (multiple choice)

A) sc.textFile("/data/file/path").map(.split(",")).map(p=>FemaleInfo
(p(O),p(1),p(2).trim.toInt)).toDF.registerTemp Table("Female infoTable") sqlContext.sq(l "select name, sum(stay Time)as stay Time from FemaleInfoTable where gender=female).filter("stay Time >=120").collect).foreach(printin)
B) val text=sc.textFile("/data/fle/path")
val data=text.filter(_.contains("female"))
Val fermaleData:RDD(String,Int) =data.map{line=>val t=line.split(',')
(t(0),t(2).toInt)
}.reduceByKey(_+_)
Val result=femaleData.filter(line=>line.2>120)
Rusult.collect.map(x=>x._1 + ',' + x._2).foreach(println)
C) val text=sc.textFile("/data/fle/path")
val data=text.filter(_.contains("female"))
Val fermaleData:RDD(String,Int) =data.map(line=>val t=line.split(',')
(t(0),t(2).toInt)
}
Val result=femaleData.filter(line=>line.2>120)
Rusult.collect.map(x=>x._1 +','+ x._2).foreach(println)
D) sc.textFile("/data/file/path").map(.split(",")).map(p=>FemaleInfo(p
(O),p(1),p(2).trim.toInt)).toDF.registerTemp Table("FemaleinfoTable")sqlContext.sql ("select name,sum(stay Time)as stay Time from FemaleInfoTable where gender= female'group by name").filter("stay Time >=120").collect).foreach(printin)

5. Which of the following is notMapReducespecialty?

A) High fault tolerance
B) Easy to program.
C) Good scalability
D) Calculate in real time

Solutions: