H13-723_V2.0 Exam Dumps - HCIP-Big Data Developer V2.0

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Question # 9

TowardsHBaseincrease in the clusterRegionServerhost, the original cluster must be stopped first, becauseHBase

Dynamic expansion is not supported.

True

False

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Question # 10

aboutHiveandHadoopThe relationship of other components, the following description is wrong?

Hivefinally store the data inHDFSmiddle

HQLable to passMapReduceperform tasks

HiveYesHadoopThe platform's data warehouse tools

HiverightHBasehave strong dependencies

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Question # 11

aboutKafkaThe characteristics of the following description are correct? (multiple choice)

KafkaIt is a high-throughput, distributed, publish-subscribe-based messaging system

KafkaPersistence of messages

KafkaApplicable to offline and online message consumption scenarios

Kafkaguarantee eachPartitionmessages in order

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Question # 12

FusionInsightHD which components are provided externallySQLor classSQLability?

(multiple choice)

HBase

Hive

Spark

Redis

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Question # 13

existMapReduceDuring application development,setMapOutputCompressorClassWhat is the role of classes?

set upmapinput compression method

set upmapHow the output is delivered

set upmapoutput compression method

set upreduceoutput compression method

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Question # 14

Below aboutZooKeeperThe description of the feature is wrong ()?

Updates sent by the client are applied in the order in which they were sent.

A message to be read by more than halfserverreceive, it will be able to successfully write to disk.

Message updates can only succeed or fail, with no intermediate states.

ZooKeeperThe number of nodes must be an odd number.

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Question # 15

due toSparkis a memory-based computing engine, therefore, aSparkThe amount of data the app can handle Can't give more than thisSparkThe total memory of the application.

True

False

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Question # 16

existSparkIn application development, which of the following codes can correctly count words?

val counts=textFile.map(line=>line.split(" ')).map(-rd=>(word,1)).reduceByKey(_+_)

val counts=textFile.flatMap(line=>line.split(" ")).map(word=>(word,1)).reduceByKey(_+_)

val counts=textFile.map(line=>line.split(" ")).map(word=>(word,1)).groupByKey()

val counts=textFile.flatmap(line=>line.split(" ")).map(-rd=>(word,1)).reduceByKey()

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