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

1. What is the role of the setMapOutputCompressorClass class in the development of MapReduce applications?

A) Set the compression method of map output
B) Set the compression method of Smap input
C) Set the delivery method of map output
D) Set the compression method of reduce output

2. There are scenarios where 500G of new data is generated by the online system every day, and these data need to be statistically summarized in dimensions such as days, weeks, and months.
Which table is suitable for Hive processing?

A) Bucket table, according to talent
B) Tilt table, select the date in the tilt field, and the tilt value is random
C) Partition + bucket table, partitioned by time, 2560 buckets per partition
D) Partition table, partition by day

3. Which of the following business applications is not suitable for Hive?

A) Non-real-time analysis (log analysis, statistical analysis)
B) Real-time online data analysis
C) Data summary (number of user clicks per day/week, click ranking)
D) Data mining (user behavior analysis, interest division, regional display)

4. 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)

5. Which Redis commands are atomic operations?

A) INCR (increase the stored digital value by one)
B) HINCRBY (add incremental increment to the field value in the hash table key)
C) All of the above
D) LPUSH (push operation on linked list)

Solutions: