Clock
Test duration:
20
min
Notes
No. of questions:
10
Tie
Level of experience:
Entry/Mid/Expert

Big Data - Pig Test

iMocha’s Big Data Pig programming skills test is the ideal pre-hire test for recruiters and hiring managers to assess candidates objectively. This test is useful for hiring job roles like Big Data Developer in Pig and Big Data Developer. Our test helps to reduce technical screening time by 80%.

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About

Big Data Pig Assessment Test

Big Data Pig online test assists recruiters and hiring managers to evaluate the Pig skills of Big Data-Pig developer before an interview. Big Data Pig is a platform used for analyzing large data sets which enable for expressing data analysis programs. Pig enables you to write complex data and is a simple SQL-like scripting language. Pig consists of two components - first is the language and the second is the runtime environment. Big Data Pig programming skills test is specially designed to check application, practical skills of a Big Data - Pig developer – as per industry standards.Apache Pig knowledge test enables employers and recruiters to identify potential Big Data-Pig developers by evaluating working skills and job readiness. For this reason, the emphasis is laid upon evaluating the knowledge of applied skills gained through real work experience, rather than theoretical knowledge.

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How it works

Test Summary

<p lang="EN-US" paraeid="{d9741699-d619-4100-911e-a1993304a516}{221}" paraid="337279069" xml:lang="EN-US">Apache Pig coding test is designed by our subject matter experts to evaluate the job skills of an applicant and their knowledge about Big Data Pig. Using powerful reporting, you can have a detailed analysis of the test results to help you make a better hiring decision and predict the candidate&rsquo;s performance.</p>

<p lang="EN-US" paraeid="{d8c60074-32d0-4f30-b2a1-efa503422cd2}{141}" paraid="84817524" xml:lang="EN-US">Pig Big Data assessment test may contain MCQs (Multiple Choice Questions), MAQs (Multiple Answer Questions), Fill in the Blanks, Descriptive, Whiteboard Questions, Audio / Video Questions, AI-LogicBox, Coding Simulations, True or False Questions, etc.</p>

Test Duration
20
No. of Questions
10
Level of Expertise
Entry/Mid/Expert
Useful for hiring
  • Big Data Developer - Pig
  • Big Data Developer
Topics Covered
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Pig Commands

iMocha’s Big Data Pig online test helps recruiters and hiring managers to evaluate candidate’s ability to use Big Data Pig commands

Pig Scripts

Our test assesses candidate’s ability to run Pig scripts which convert into a series of MapReduce jobs that are run on the Apache Hadoop cluster
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Optimization in Pig

This test evaluates candidate’s understanding about optimization rules which help to improve performance and efficiency in Big Data
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Sample Question
Choose from our 100,000+ questions library or add your own questions to make powerful custom tests.

Question types :

Multiple Option

Topic:

Difficulty:

Hard


Q 1. You have been asked to train « Naive Bayes model for spam detection dataset

Using Python command, what wil you add in the given code at XXXX to deciore, it and train your modal for the given dataset?

import numpy os np

X= nporroyll 3. 2b[1 21 [2.21.[3,21)
Y= nporray(fi.a,a.1))

XXXX

from skinamnaive_bayas import

Gaussianna

otf = Gaussianne()

ceataie(x ¥)

from skinamnaive_bayas import

Gaussianna

otf = Gaussianne()

ceataie(x ¥)

from skinamnaive_bayas import

Gaussianna

otf = Gaussianne()

ceataie(x ¥)

from skinamnaive_bayas import

Gaussianna

otf = Gaussianne()

ceataie(x ¥)

Question types :

Multiple Option

Topic:

Machine teaming

Difficulty:

Hard


Q 1. Q2 Lets assume that you have 6 Pandas DataFrome(af) in the below tole.

Index Code Open High
0 AAPL 19877 70074
1 AAPL 19877 70074
2 AAPL 19877 70074
3 AAPL 19877 70074
4 AAPL 19877 70074

When the following commend is executed, what hoppens to the DatoFrome?

df= defiinat()
Y= nporray(fi.a,a.1))

Index Code Open High
0 AAPL 19877 70074
1 AAPL 19877 70074
2 AAPL 19877 70074
3 AAPL 19877 70074
4 AAPL 19877 70074
Index Code Open High
0 AAPL 19877 70074
1 AAPL 19877 70074
2 AAPL 19877 70074
3 AAPL 19877 70074
4 AAPL 19877 70074
it gives an error becouse of the third row which has on empty string.
None of these

Question types :

Multiple Option

Topic:

Difficulty:

Easy

Question:

Assume we use datafu lib in pig:

DEFINE EmptyBagToNullFields datafu.pig.bags.EmptyBagToNullFields();
data = FOREACH (COGROUP input1 BY key, input2 BY key, input3 BY key) GENERATE
    FLATTEN(input1),
      FLATTEN(EmptyBagToNullFields(input2)),
      FLATTEN(EmptyBagToNullFields(input3));

 Which script will define macro similar to above script?


Options

  • DEFINE myFunction(relation1, key1, relation2, key2, relation3, key3) returns result&nbsp;<br />{<br />&nbsp;&nbsp; &nbsp;A = GROUP $relation1 BY $key1, $relation2 BY $key2, $relation3 BY $key3;<br />&nbsp;&nbsp; &nbsp;$result = FOREACH A GENERATE<br />&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;FLATTEN($relation1),<br />&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;FLATTEN(EmptyBagToNullFields($relation2)),<br />&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;FLATTEN(EmptyBagToNullFields($relation3));<br />}
  • DEFINE myFunction(relation1, key1, relation2, key2, relation3, key3) returns result&nbsp;<br />{<br />&nbsp;&nbsp; &nbsp;A = JOIN $relation1 BY $key1, $relation2 BY $key2, $relation3 BY $key3;<br />&nbsp;&nbsp; &nbsp;$result = FOREACH A GENERATE<br />&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;FLATTEN($relation1),<br />&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;FLATTEN(EmptyBagToNullFields($relation2)),<br />&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;FLATTEN(EmptyBagToNullFields($relation3));<br />}
  • Cannot replace to have same logic with above script
  • DEFINE myFunction(relation1, key1, relation2, key2, relation3, key3) returns result&nbsp;<br />{<br />&nbsp;&nbsp; &nbsp;A = COGROUP $relation1 BY $key1, $relation2 BY $key2, $relation3 BY $key3;<br />&nbsp;&nbsp; &nbsp;$result = FOREACH A GENERATE<br />&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;FLATTEN($relation1),<br />&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;FLATTEN(EmptyBagToNullFields($relation2)),<br />&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;FLATTEN(EmptyBagToNullFields($relation3));<br />}
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