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Huawei H13-723-ENU Exam Syllabus Topics:

Huawei HCIP-Big Data Developer Sample Questions:

1. Which of the following descriptions about the characteristics of Kafka is correct? (multiple choice)

A) Kafka can ensure that the messages in each Partition are in order
B) Kafka can achieve message persistence
C) Kafka is suitable for offline and online message consumption scenarios
D) Kafka is a high-throughput, distributed, publish-and-subscribe messaging system

2. When indexing in Solr, which of the following code snippets can significantly improve indexing performance? (multiple choice)

A) SolrlnputDocument doc = new SolrlnputDocument(); doc.addField ("id", i.toString()); doc.addField ("name", "Zhang San" + i); doc.addField ("features", "test" + ); doc.addField ("price", (float) i * 1.01); cloudSolrClient.add (doc); cloudSolrClient.commit();
B) if (updateRequest.getDocuments().size() >= 1000) { cloudSolrClient.request (updateRequest, collName); updateRequest.clear(); }
C) if (updateRequest.getDocumentsMap().size() >= 10000) { cloudSolrClient.request (updateRequest, collName); updateRequest.clear(); }
D) lbClient.setRequestWriter (new BinaryRequestWriter());

3. In FusionInsight HD, regarding the secondary development of Hive UDF, which of the following descriptions is correct? (multiple choice)

A) In a secure cluster, user-defined UDFs are recommended to be created before each use.
B) User-defined UDF can add deterministic and statefull annotations according to the actual situation.
C) User-defined UDF is not allowed to add information such as summary and status.
D) Before the user-defined UDF is used, it needs to be created in the Hive system.

4. In FusionInsight HD, about Hive and Sparksql, which of the following descriptions are correct? (multiple choice)

A) Both can provide SQL or SQL-like capabilities externally
B) Both can run on the MapReduce computing framework
C) Both can run on the Spark computing framework
D) Both can access data on HBase through the table field and HBase field mapping method

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) 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)
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)

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