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Huawei HCIP-Big Data Developer Sample Questions:

1. What methods does FusionInsight HD support to submit Oozie jobs? (multiple choice)

A) Shell commands
B) Rest API
C) Java API
D) Hue UI

2. In FusionInsight HD, regarding Hive's data loading function (importing data through Hive's LOAD command), what is wrong in the following description?

A) It can be directly loaded to the hive table from the local hard disk of the node where the client is located
B) Hive's data loading process will not parse the specific file content, mainly the process of file transfer
C) It can be directly loaded to the hive table from the local hard disk of the node where HiveServer is located
D) It can be loaded directly to the hive table from HDFS

3. During the Solr application development of FusionInsight HD products, you can verify the Collection through the Solr Admin UI.
Which of the following statements about Solr Admin UI is correct? (multiple choice)

A) Click Cloud to view the distribution and status of each ShardReplica under each Collection
B) Solr user group, Solr admin role and Super group users can access Solr Admin UI
C) Click Tree under Cloud to view metadata information such as Collections, configuration sets and live_nodes in Solr Cloud
D) Enter the Core Overview interface of the Shard Replica of a Collection, you can view the actual number of documents, storage size and location information of the Replica index

4. In FusionInsight HD, in which ways can I view the debugging results of Oozie jobs? (multiple choice)

A) Oozie's own web interface can also view job execution results
B) View program running results through Hue's workflow dashboard
C) Use Hue's file browser to check whether the desired file is generated in the HDFS specified directory
D) Check whether the returned result of Oozie's JAVA API meets expectations

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) 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)
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) }.reduceByKey(_ + _) 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 ' group by name " ).filter( " stayTime >= 120 " ).collect().foreach(println)
D) 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)

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