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Huawei H13-723-ENU Exam Syllabus Topics:

Huawei HCIP-Big Data Developer Sample Questions:

1. Set the data block storage size to 128M. When the HDFS client writes a file, when writing a 100M file, how much storage space is actually occupied?

A) 64M
B) 100M
C) 50M
D) 128M

2. Regarding the disaster tolerance of Streaming, which of the following statements is correct? (multiple choice)

A) After the Supervisor process exits, it can be automatically recognized and pulled up by Nimbus without affecting the running business.
B) When a node fails, tasks on that node will be reassigned to other normal nodes without manual intervention.
C) After Nimbus fails, the standby Nimbus will automatically take over, without affecting the running business.
D) Worker can be automatically pulled up by Supervisor after abnormal exit without manual intervention.

3. In Spark, which statement about broadcast variables is correct?

A) The broadcast variable is saved in each Executor
B) Broadcast variables are saved in each Task
C) Broadcast variables are read-only
D) Broadcast variables are generated by the SparkContext.broadcast() method

4. In the MapReduce development framework, what is the function of the InputFormat class?

A) Combine key-value pairs with the same key value in the result of the map phase
B) Sort and merge the results of the map stage
C) Combine the results of all map stages to get the final result
D) Split the input data into individual splits, and further split the split into <key, value> pairs, which are then used as the input of the map function

5. There are the following business scenarios: the user's online log files have been stored on HDFS, and the log file content format is: each online record has three fields, namely name, gender, and online time, separated by "," between the fields; All female netizens who spend more than two hours online are required to print out.
Which of the following code fragments can achieve the above business scenarios? (multiple choice)

A) val text = sc.textFile("/data/file/path") val data = text.filter(_.contains("female")) val femaleData:RDD[(String,Int)] = data.map{Iine => val t = line.split( ' , ' ) (t(0),t(2).tolnt) }.reduceByKey(_ + _) val result = femaleData.filter(line => line._2> 120) result.collect().map(x => x._1 + ' , ' + x._2).foreach(println)
B) val text = sc.textFile("/data/file/path") val data = text.filter(_.contains("female")) val femaleData:RDD[(String,Int)] = data.map{Iine => val t = line.split( ' , ' ) (t(0),t(2).tolnt) }. val result = femaleData.filter(line => line._2> 120) result.collect().map(x => x._1 + ' , ' + x._2).foreach(println)
C) sc.textFile( " /data/file/path " .map(_.split( " , " ).map(p => Femalelnfo(p(0), p(1), p (2) .trim.tolnt)).toDF.registerTempTable("FemalelnfoTable") sqlContext.sql("select name,sum(stayTime) as stayTime from FemalelnfoTable where gender = ' Female '" ).filter( " stayTime >= 120 " ).collect().foreach(println)
D) sc.textFile( " /data/file/path " .map(_.split( " , " ).map(p => Femalelnfo(p(0), p(1), p (2) .trim.tolnt)).toDF.registerTempTable("FemalelnfoTable") sqlContext.sql("select name,sum(stayTime) as stayTime from FemalelnfoTable where gender = ' Female ' group by name " ).filter( " stayTime >= 120 " ).collect().foreach(println)

Solutions: