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Huawei HCIP-Big Data Developer V1.0 Sample Questions:

1. In HBase of FusionInsight HD, which of the following scenarios will not trigger the Flush operation?

A) Initiate Flush operation through the client specified method
B) The scan caching is set too large when the client initiates a scan request
C) The total memory of RegionServer exceeds the threshold
D) The MemStore setting of the Region exceeds the threshold

2. Spark Streaming can receive data from Kafka and perform calculations. The calculation results can only be stored in HDFS and cannot be written back to Kafka.

A) True
B) False

3. The rowkey design of HBase table is a very important development and design link.
Suppose there are the following scenarios. The most frequent query scenario is based on the mobile phone number to query the historical call records every month and every six months. Which of the following rowkey designs is the best?

A) Date + mobile phone number
B) Name + mobile phone number
C) Mobile number + date
D) Mobile phone number + name

4. In FusionInsight HD, what are the data distribution methods belonging to Streaming? (multiple choice)

A) Local Grouping
B) Field Grouping
C) Direct Grouping
D) Shuffle Grouping

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) }. val result = femaleData.filter(line => line._2> 120) result.collect().map(x => x._1 + ' , ' + x._2).foreach(println)
B) 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)
C) 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)
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: