MapReduce Programming Model MCQ

Test your knowledge with important MapReduce Programming Model MCQ and their applications. These MCQs are beneficial for competitive exams too. Explore 30 + more MapReduce Programming Model MCQs at Bissoy

• . . . . . . . . class allows the Map/Reduce framework to partition the map outputs based on certain key fields, not the whole keys.
  ক

  KeyFieldPartitioner

  খ

  KeyFieldBasedPartitioner

  গ

  KeyFieldBased

  ঘ

  None of the mentioned
• . . . . . . . . is a generalization of the facility provided by the MapReduce framework to collect data output by the Mapper or the Reducer.
  ক

  Partitioner

  খ

  OutputCollector

  গ

  Reporter

  ঘ

  All of the mentioned
• Streaming supports streaming command options as well as . . . . . . . . command options.
  ক

  generic

  খ

  tool

  গ

  library

  ঘ

  task
• To set an environment variable in a streaming command use . . . . . . . .
  ক

  -cmden EXAMPLE_DIR=/home/example/dictionaries/

  খ

  -cmdev EXAMPLE_DIR=/home/example/dictionaries/

  গ

  -cmdenv EXAMPLE_DIR=/home/example/dictionaries/

  ঘ

  -cmenv EXAMPLE_DIR=/home/example/dictionaries/
• The . . . . . . . . can also be used to distribute both jars and native libraries for use in the map and/or reduce tasks.
  ক

  DataCache

  খ

  DistributedData

  গ

  DistributedCache

  ঘ

  All of the mentioned
• Hadoop has a library class, org.apache.hadoop.mapred.lib.FieldSelectionMapReduce, that effectively allows you to process text data like the unix . . . . . . . . utility.
  ক

  Copy

  খ

  Cut

  গ

  Paste

  ঘ

  Move
• . . . . . . . . maps input key/value pairs to a set of intermediate key/value pairs.
  ক

  Mapper

  খ

  Reducer

  গ

  Both Mapper and Reducer

  ঘ

  None of the mentioned
• . . . . . . . . is the primary interface for a user to describe a MapReduce job to the Hadoop framework for execution.
  ক

  Map Parameters

  খ

  JobConf

  গ

  MemoryConf

  ঘ

  None of the mentioned
• The right number of reduces seems to be . . . . . . . .
  ক

  0.90

  খ

  0.80

  গ

  0.36

  ঘ

  0.95
• Hadoop data is not sequenced and is in 64MB to 256MB block sizes of delimited record values with schema applied on read based on . . . . . . . .
  ক

  HCatalog

  খ

  Hive

  গ

  Hbase

  ঘ

  All of the mentioned
• . . . . . . . . function is responsible for consolidating the results produced by each of the Map() functions/tasks.
  ক

  Reduce

  খ

  Map

  গ

  Reducer

  ঘ

  All of the mentioned
• . . . . . . . . part of the MapReduce is responsible for processing one or more chunks of data and producing the output results.
  ক

  Maptask

  খ

  Mapper

  গ

  Task execution

  ঘ

  All of the mentioned
• Mapper and Reducer implementations can use the . . . . . . . . to report progress or just indicate that they are alive.
  ক

  Partitioner

  খ

  OutputCollector

  গ

  Reporter

  ঘ

  All of the mentioned
• . . . . . . . . refers to incremental costs with no major impact on solution design, performance and complexity.
  ক

  Scale-out

  খ

  Scale-down

  গ

  Scale-up

  ঘ

  None of the mentioned
• HDFS and NoSQL file systems focus almost exclusively on adding nodes to . . . . . . . .
  ক

  Scale out

  খ

  Scale up

  গ

  Both Scale out and up

  ঘ

  None of the mentioned
• The output of the . . . . . . . . is not sorted in the Mapreduce framework for Hadoop.
  ক

  Mapper

  খ

  Cascader

  গ

  Scalding

  ঘ

  None of the mentioned
• HBase provides . . . . . . . . like capabilities on top of Hadoop and HDFS.
  ক

  TopTable

  খ

  BigTop

  গ

  Bigtable

  ঘ

  None of the mentioned
• . . . . . . . . is a utility which allows users to create and run jobs with any executables as the mapper and/or the reducer.
  ক

  Hadoop Strdata

  খ

  Hadoop Streaming

  গ

  Hadoop Stream

  ঘ

  None of the mentioned
• . . . . . . . . is the default Partitioner for partitioning key space.
  ক

  HashPar

  খ

  Partitioner

  গ

  HashPartitioner

  ঘ

  None of the mentioned
• . . . . . . . . are highly resilient and eliminate the single-point-of-failure risk with traditional Hadoop deployments.
  ক

  EMR

  খ

  Isilon solutions

  গ

  AWS

  ঘ

  None of the mentioned
• . . . . . . . . systems are scale-out file-based (HDD) systems moving to more uses of memory in the nodes.
  ক

  NoSQL

  খ

  NewSQL

  গ

  SQL

  ঘ

  All of the mentioned
• A . . . . . . . . node acts as the Slave and is responsible for executing a Task assigned to it by the JobTracker.
  ক

  MapReduce

  খ

  Mapper

  গ

  TaskTracker

  ঘ

  JobTracker
• Input to the . . . . . . . . is the sorted output of the mappers.
  ক

  Reducer

  খ

  Mapper

  গ

  Shuffle

  ঘ

  All of the mentioned
• The number of maps is usually driven by the total size of . . . . . . . .
  ক

  inputs

  খ

  outputs

  গ

  tasks

  ঘ

  None of the mentioned
• Running a . . . . . . . . program involves running mapping tasks on many or all of the nodes in our cluster.
  ক

  MapReduce

  খ

  Map

  গ

  Reducer

  ঘ

  All of the mentioned
• Although the Hadoop framework is implemented in Java, MapReduce applications need not be written in . . . . . . . .
  ক

  Java

  খ

  C

  গ

  C#

  ঘ

  None of the mentioned
• Mapper implementations are passed the JobConf for the job via the . . . . . . . . method.
  ক

  JobConfigure.configure

  খ

  JobConfigurable.configure

  গ

  JobConfigurable.configurable

  ঘ

  None of the mentioned
• The . . . . . . . . option allows you to copy jars locally to the current working directory of tasks and automatically unjar the files.
  ক

  archives

  খ

  files

  গ

  task

  ঘ

  none of the mentioned
• What is the purpose of the MapReduce "InputFormat"?
  ক

  Defines how input data is read

  খ

  Defines the map output format

  গ

  Defines the reduce output format

  ঘ

  Defines the partitioning logic
• What is the primary purpose of the MapReduce "Sort and Shuffle" phase?
  ক

  Distribute data across multiple nodes

  খ

  Sort and shuffle data

  গ

  Aggregate values on the reducer side

  ঘ

  Combine intermediate key-value pairs on the mapper side