Big Data Pig Assessment Test

Topics covered

Pig Commands, Optimization in Pig, Pig Scripts

  • MAQ (Multiple Answer Question)
  • MCQ (Multiple Choice Question)
  • Descriptive Question
  • White Board Simulator
  • Coding Simulator
  • Audio Question
  • Video Question
  • Case Study Question

Big Data Pig Online Test assists Recruiters and Hiring Manager 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 to write complex data and is a simple SQL-like scripting language. Pig consists of two components - first is the language and 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.

Pig Big Data Assessment Test contains questions on following Topics:
 

  • Pig Commands

  • Optimization in Pig

  • Pig Scripts

 
Apache Pig Coding Test is designed by our subject matter experts to evaluate the job skills of 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’s performance.

The test contains MCQ's (Multiple Choice Questions), MAQ's (Multiple Answer Questions), Fill in the Blank, Descriptive, True or False.

This pre-employment test is useful for hiring:
  • Big Data Developer - Pig
  • Big Data Developer

Test details:

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.

Test Details: 20 minutes 

15 Application Questions

05 Theory Questions 


The combination of Application and Theory questions helps to evaluate Technical as well as practical Skills of Candidates

Big Data - Pig Test

Question #1 of 3

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?

    • DEFINE myFunction(relation1, key1, relation2, key2, relation3, key3) returns result 
      {
          A = GROUP $relation1 BY $key1, $relation2 BY $key2, $relation3 BY $key3;
          $result = FOREACH A GENERATE
              FLATTEN($relation1),
              FLATTEN(EmptyBagToNullFields($relation2)),
              FLATTEN(EmptyBagToNullFields($relation3));
      }

    • DEFINE myFunction(relation1, key1, relation2, key2, relation3, key3) returns result 
      {
          A = JOIN $relation1 BY $key1, $relation2 BY $key2, $relation3 BY $key3;
          $result = FOREACH A GENERATE
              FLATTEN($relation1),
              FLATTEN(EmptyBagToNullFields($relation2)),
              FLATTEN(EmptyBagToNullFields($relation3));
      }

    • Cannot replace to have same logic with above script

    • DEFINE myFunction(relation1, key1, relation2, key2, relation3, key3) returns result 
      {
          A = COGROUP $relation1 BY $key1, $relation2 BY $key2, $relation3 BY $key3;
          $result = FOREACH A GENERATE
              FLATTEN($relation1),
              FLATTEN(EmptyBagToNullFields($relation2)),
              FLATTEN(EmptyBagToNullFields($relation3));
      }

Question #2 of 3

A = load 'Data1' as (name:chararray, team:chararray, position:bag{t:(p:chararray)}, bat:map[]);
B = foreach A generate name, team, position, bat#'batting_average' as batavg;
C = group B by team;
avgC  = foreach C generate group, AVG(B.batavg);
store avgC into 'by_team';
flattenData = foreach B generate name, team, flatten(position) as position, batavg;
D = group flattenData by position parallel 100;
avgD = foreach D generate group, AVG(flattenData.batavg);
store avgD into 'Rank';

How many reduce will run at D relation?

    • 100

    • Not enough information

    • Depend on Data1 file

    • Depend on flattenData relation

Question #3 of 3

A = COGROUP input1 BY key, input2 BY key, input3 BY key;
B = FOREACH A GENERATE
    FLATTEN(input1), -- left join on this
     FLATTEN((IsEmpty(input2) ? TOBAG(TOTUPLE((int)null,(int),null)) : input2))
       AS (input2::key,input2::val),
    FLATTEN((IsEmpty(input3) ? TOBAG(TOTUPLE((int)null,(int),null)) : input3))
       AS (input3::key,input3::val);
   
Which script is similar with above script?

    • A = JOIN input1 BY key LEFT, input2 BY key;
      B = JOIN A BY input1::key LEFT, input3 BY key;

    • A = JOIN input1 BY key LEFT, input3 BY key;
      B = JOIN A BY input1::key LEFT, input2 BY key;

    • Cannot replace to have same logic with above script

    • A = JOIN input1 BY key, input3 BY key;
      B = JOIN A BY input1::key, input2 BY key; 

SAMPLE REPORT
Candidate Name: David Messi Test Date: 28-May-2014
Test Name: Big Data - Pig Test Test Start Time: 13.30

Score: 18 Out Of 20

Percentage: 90

Result: PASS

passing score image
SECTION PERFORMANCE
Section Name FAIL PASS
Pig
Score: 18/20
STRENGTH AND WEAKNESS
Strength Pig
Weakness -
* Note: If score <= 40 then its a Weakness. If score >= 80 then its a Strength.
ONLINE PROCTORING
0%
10 20 30 40 50 60 70 80 90 100%
Tolerable Limit Not acceptable
Window Violation: 0 | Time Violation: 0 secs
CANDIDATE DETAILS
Gender : Male Phone Number : +1-541-754-3010
Total Experience (Years) : 3 Total Experience (Months) : 5
Skill Set : Pig Current Employer : Interview Mocha
Current Job Title : Solution Architect Current Salary : 600.00
Expected Salary : 1000000.00 Current Location : Chicago

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