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Best Practices And Optimization Of Hadoop MCQs

Practice Best Practices And Optimization Of Hadoop MCQs for competitive exams.

Best Practices And Optimization Of Hadoop MCQs

Practice questions from this topic.

The IBM . . . . . . . . Platform provides all the foundational building blocks of trusted information, including data integration, data warehousing, master data management, big data and information governance.

  1. A. InfoStream
  2. B. InfoSphere
  3. C. InfoSurface
  4. D. InfoData
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. . . . . . . . is an open source system for expressive, declarative, fast, and efficient data analysis.

  1. A. Flume
  2. B. Flink
  3. C. Flex
  4. D. ESME
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Hadoop clusters running on Amazon EMR use . . . . . . . . instances as virtual Linux servers for the master and slave nodes.

  1. A. EC2
  2. B. EC3
  3. C. EC4
  4. D. None of the mentioned
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. . . . . . . . is a distributed computing framework based on BSP.

  1. A. HCataMan
  2. B. HCatlaog
  3. C. Hama
  4. D. All of the mentioned
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Apache Bigtop uses . . . . . . . . for continuous integration testing.

  1. A. Jenkinstop
  2. B. Jerry
  3. C. Jenkins
  4. D. None of the mentioned
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. . . . . . . . is a online NoSQL developed by Cloudera.

  1. A. HCatalog
  2. B. Hbase
  3. C. Imphala
  4. D. Oozie
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If the failure is of . . . . . . . . nature, Oozie will suspend the workflow job.

  1. A. transient
  2. B. non-transient
  3. C. permanent
  4. D. all of the mentioned
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Each kafka partition has one server which acts as the . . . . . . . .

  1. A. leaders
  2. B. followers
  3. C. staters
  4. D. all of the mentioned
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Stratos will be a polyglot . . . . . . . . framework.

  1. A. Daas
  2. B. PaaS
  3. C. Saas
  4. D. Raas
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. . . . . . . . is a distributed, fault-tolerant, and high-performance realtime computation system.

  1. A. Knife
  2. B. Storm
  3. C. Sqoop
  4. D. Lucene
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InfoSphere DataStage uses a client/server design where jobs are created and administered via a . . . . . . . . client against a central repository on a server.

  1. A. Ubuntu
  2. B. Windows
  3. C. Debian
  4. D. Solaris
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The ability of Hadoop to efficiently process large volumes of data in parallel is called . . . . . . . . processing.

  1. A. batch
  2. B. stream
  3. C. time
  4. D. all of the mentioned
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