Data Processing With Pig MCQ

Test your knowledge with important Data Processing With Pig MCQ and their applications. These MCQs are beneficial for competitive exams too. Explore 30 + more Data Processing With Pig MCQs at Bissoy

• In comparison to SQL, Pig uses . . . . . . . .
  ক

  Lazy evaluation

  খ

  ETL

  গ

  Supports pipeline splits

  ঘ

  All of the mentioned
• Pig Latin is . . . . . . . . and fits very naturally in the pipeline paradigm while SQL is instead declarative.
  ক

  functional

  খ

  procedural

  গ

  declarative

  ঘ

  all of the mentioned
• Which of the following code is used to find scripts that use only the default parallelism?
  ক

  a = load '/mapred/history/done' using HadoopJobHistoryLoader() as (j:map, m:map, r:map);<br>b = foreach a generate (Chararray) j#'STATUS' as status, j#'PIG_SCRIPT_ID' as id, j#'USER' as user, j#'JOBNAME' as script_name, j#'JOBID' as job;<br>c = filter b by status != 'SUCCESS';<br>dump c;

  খ

  a = load '/mapred/history/done' using HadoopJobHistoryLoader() as (j:map, m:map, r:map);<br>b = foreach a generate j#'PIG_SCRIPT_ID' as id, j#'USER' as user, j#'JOBNAME' as script_name, (Long) r#'NUMBER_REDUCES' as reduces;<br>c = group b by (id, user, script_name) parallel 10;<br>d = foreach c generate group.user, group.script_name, MAX(b.reduces) as max_reduces;<br>e = filter d by max_reduces == 1;<br>dump e;

  গ

  a = load '/mapred/history/done' using HadoopJobHistoryLoader() as (j:map, m:map, r:map);<br>b = foreach a generate j#'PIG_SCRIPT_ID' as id, j#'USER' as user, j#'QUEUE_NAME' as queue;<br>c = group b by (id, user, queue) parallel 10;<br>d = foreach c generate group.user, group.queue, COUNT(b);<br>dump d;

  ঘ

  None of the mentioned
• Which of the following script is used to check scripts that have failed jobs?
  ক

  a = load '/mapred/history/done' using HadoopJobHistoryLoader() as (j:map, m:map, r:map);<br>b = foreach a generate (Chararray) j#'STATUS' as status, j#'PIG_SCRIPT_ID' as id, j#'USER' as user, j#'JOBNAME' as script_name, j#'JOBID' as job;<br>c = filter b by status != 'SUCCESS';<br>dump c;

  খ

  a = load '/mapred/history/done' using HadoopJobHistoryLoader() as (j:map, m:map, r:map);<br>b = foreach a generate j#'PIG_SCRIPT_ID' as id, j#'USER' as user, j#'JOBNAME' as script_name, (Long) r#'NUMBER_REDUCES' as reduces;<br>c = group b by (id, user, script_name) parallel 10;<br>d = foreach c generate group.user, group.script_name, MAX(b.reduces) as max_reduces;<br>e = filter d by max_reduces == 1;<br>dump e;

  গ

  a = load '/mapred/history/done' using HadoopJobHistoryLoader() as (j:map, m:map, r:map);<br>b = foreach a generate j#'PIG_SCRIPT_ID' as id, j#'USER' as user, j#'QUEUE_NAME' as queue;<br>c = group b by (id, user, queue) parallel 10;<br>d = foreach c generate group.user, group.queue, COUNT(b);<br>dump d;

  ঘ

  None of the mentioned
• Which of the following script determines the number of scripts run by user and queue on a cluster?
  ক

  a = load '/mapred/history/done' using HadoopJobHistoryLoader() as (j:map, m:map, r:map);<br>b = foreach a generate (Chararray) j#'STATUS' as status, j#'PIG_SCRIPT_ID' as id, j#'USER' as user, j#'JOBNAME' as script_name, j#'JOBID' as job;<br>c = filter b by status != 'SUCCESS';<br>dump c;

  খ

  a = load '/mapred/history/done' using HadoopJobHistoryLoader() as (j:map, m:map, r:map);<br>b = foreach a generate j#'PIG_SCRIPT_ID' as id, j#'USER' as user, j#'JOBNAME' as script_name, (Long) r#'NUMBER_REDUCES' as reduces;<br>c = group b by (id, user, script_name) parallel 10;<br>d = foreach c generate group.user, group.script_name, MAX(b.reduces) as max_reduces;<br>e = filter d by max_reduces == 1;<br>dump e;

  গ

  a = load '/mapred/history/done' using HadoopJobHistoryLoader() as (j:map, m:map, r:map);<br>b = foreach a generate j#'PIG_SCRIPT_ID' as id, j#'USER' as user, j#'QUEUE_NAME' as queue;<br>c = group b by (id, user, queue) parallel 10;<br>d = foreach c generate group.user, group.queue, COUNT(b);<br>dump d;

  ঘ

  None of the mentioned
• Which of the following find the running time of each script (in seconds)?
  ক

  a = load '/mapred/history/done' using HadoopJobHistoryLoader() as (j:map, m:map, r:map);<br>b = foreach a generate j#'PIG_SCRIPT_ID' as id, j#'USER' as user, j#'JOBNAME' as script_name,<br>       (Long) j#'SUBMIT_TIME' as start, (Long) j#'FINISH_TIME' as end;<br>c = group b by (id, user, script_name)<br>d = foreach c generate group.user, group.script_name, (MAX(b.end) - MIN(b.start)/1000;dump d;

  খ

  a = load '/mapred/history/done' using HadoopJobHistoryLoader() as (j:map, m:map, r:map);<br>b = for a generate j#'PIG_SCRIPT_ID' as id, j#'USER' as user, j#'JOBNAME' as script_name,<br>       (Long) j#'SUBMIT_TIME' as start, (Long) j#'FINISH_TIME' as end;<br>c = group b by (id, user, script_name)<br>d = for c generate group.user, group.script_name, (MAX(b.end) - MIN(b.start)/1000;dump d;

  গ

  a = load '/mapred/history/done' using HadoopJobHistoryLoader() as (j:map, m:map, r:map);<br>b = foreach a generate j#'PIG_SCRIPT_ID' as id, j#'USER' as user, j#'QUEUE_NAME' as queue;<br>c = group b by (id, user, queue) parallel 10;<br>d = foreach c generate group.user, group.queue, COUNT(b);<br>dump d;

  ঘ

  All of the mentioned
• Which of the following scripts that generate more than three MapReduce jobs?
  ক

  a = load '/mapred/history/done' using HadoopJobHistoryLoader() as (j:map, m:map, r:map);<br>b = group a by (j#'PIG_SCRIPT_ID', j#'USER', j#'JOBNAME');<br>c = for b generate group.$1, group.$2, COUNT(a);<br>d = filter c by $2 &gt; 3;<br>dump d;

  খ

  a = load '/mapred/history/done' using HadoopJobHistoryLoader() as (j:map, m:map, r:map);<br>b = display a by (j#'PIG_SCRIPT_ID', j#'USER', j#'JOBNAME');<br>c = foreach b generate group.$1, group.$2, COUNT(a);<br>d = filter c by $2 &gt; 3;<br>dump d;

  গ

  a = load '/mapred/history/done' using HadoopJobHistoryLoader() as (j:map, m:map, r:map);<br>b = group a by (j#'PIG_SCRIPT_ID', j#'USER', j#'JOBNAME');<br>c = foreach b generate group.$1, group.$2, COUNT(a);<br>d = filter c by $2 &gt; 3;<br>dump d;

  ঘ

  None of the mentioned
• . . . . . . . . are scanned in the order they are specified on the command line.
  ক

  Command line parameters

  খ

  Parameter files

  গ

  Declare and default preprocessors

  ঘ

  Both parameter files and command line parameters
• Which of the following is correct syntax for parameter substitution using cmd?
  ক

  pig {-param param_name = param_value | -param_file file_name} script

  খ

  {%declare | %default} param_name param_value

  গ

  {%declare | %default} param_name param_value cmd

  ঘ

  All of the mentioned
• Use the . . . . . . . . command to run a Pig script that can interact with the Grunt shell (interactive mode).
  ক

  fetch

  খ

  declare

  গ

  run

  ঘ

  all of the mentioned
• A loader implementation should implement . . . . . . . . if casts (implicit or explicit) from DataByteArray fields to other types need to be supported.
  ক

  LoadPushDown

  খ

  LoadMetadata

  গ

  LoadCaster

  ঘ

  All of the mentioned
• . . . . . . . . method tells LoadFunc which fields are required in the Pig script.
  ক

  pushProjection()

  খ

  relativeToAbsolutePath()

  গ

  prepareToRead()

  ঘ

  none of the mentioned
• . . . . . . . . method enables the RecordReader associated with the InputFormat provided by the LoadFunc is passed to the LoadFunc.
  ক

  getNext()

  খ

  relativeToAbsolutePath()

  গ

  prepareToRead()

  ঘ

  all of the mentioned
• The loader should use . . . . . . . . method to communicate the load information to the underlying InputFormat.
  ক

  relativeToAbsolutePath()

  খ

  setUdfContextSignature()

  গ

  getCacheFiles()

  ঘ

  setLocation()
• . . . . . . . . return a list of hdfs files to ship to distributed cache.
  ক

  relativeToAbsolutePath()

  খ

  setUdfContextSignature()

  গ

  getCacheFiles()

  ঘ

  getShipFiles()
• . . . . . . . . method will be called by Pig both in the front end and back end to pass a unique signature to the Loader.
  ক

  relativeToAbsolutePath()

  খ

  setUdfContextSignature()

  গ

  getCacheFiles()

  ঘ

  getShipFiles()
• . . . . . . . . abstract class has three main methods for loading data and for most use cases it would suffice to extend it.
  ক

  Load

  খ

  LoadFunc

  গ

  FuncLoad

  ঘ

  None of the mentioned
• PigUnit runs in Pig's . . . . . . . . mode by default.
  ক

  local

  খ

  tez

  গ

  mapreduce

  ঘ

  none of the mentioned
• . . . . . . . . is a simple xUnit framework that enables you to easily test your Pig scripts.
  ক

  PigUnit

  খ

  PigXUnit

  গ

  PigUnitX

  ঘ

  All of the mentioned
• The . . . . . . . . class mimics the behavior of the Main class but gives users a statistics object back.
  ক

  PigRun

  খ

  PigRunner

  গ

  RunnerPig

  ঘ

  None of the mentioned
• . . . . . . . . is a framework for collecting and storing script-level statistics for Pig Latin.
  ক

  Pig Stats

  খ

  PStatistics

  গ

  Pig Statistics

  ঘ

  None of the mentioned
• . . . . . . . . operator is used to view the step-by-step execution of a series of statements.
  ক

  ILLUSTRATE

  খ

  DESCRIBE

  গ

  STORE

  ঘ

  EXPLAIN
• . . . . . . . . operator is used to review the schema of a relation.
  ক

  DUMP

  খ

  DESCRIBE

  গ

  STORE

  ঘ

  EXPLAIN
• $ pig -x tez_local ... will enable . . . . . . . . mode in Pig.
  ক

  Mapreduce

  খ

  Tez

  গ

  Local

  ঘ

  None of the mentioned
• You can run Pig in interactive mode using the . . . . . . . . shell.
  ক

  Grunt

  খ

  FS

  গ

  HDFS

  ঘ

  None of the mentioned
• You can run Pig in batch mode using . . . . . . . .
  ক

  Pig shell command

  খ

  Pig scripts

  গ

  Pig options

  ঘ

  All of the mentioned
• What is the role of the Pig Latin SPLIT statement in Apache Pig?
  ক

  Load data into a relation

  খ

  Perform data analytics in Hadoop

  গ

  Manage computation resources

  ঘ

  Split data into multiple relations
• In Pig, what is the purpose of the Pig Latin SAMPLE statement?
  ক

  Manage computation resources

  খ

  Perform data analytics in Hadoop

  গ

  Generate a random sample of data

  ঘ

  Load data into a relation
• What is the primary role of the Pig Latin SUM() function?
  ক

  Load data into a relation

  খ

  Calculate the sum of values

  গ

  Manage computation resources

  ঘ

  Load data into a relation
• In Pig, what does the term "PiggyBank" refer to?
  ক

  A data type in Pig

  খ

  A bag of tuples

  গ

  A collection of user-contributed Pig UDFs

  ঘ

  A relation in Pig