Why you should use MapReduce Online Test?

The MapReduce -test makes use of scenario-based questions to test for on-the-job skills as opposed to theoretical knowledge, ensuring that candidates who do well on this screening test have the relavant skills. The questions are designed to covered following on-the-job aspects: Evne til at implementere MapReduce -algoritmer til big -databehandling Færdighed i Hadoop -økosystem og dets komponenter Forståelse af distribuerede computerprincipper Kapacitet til at analysere data ved hjælp af MapReduce -teknikker Kendskab til Hadoops arkitektur og dens rolle i big databehandling Ekspertise inden for databehandling ved hjælp af MapReduce -rammer Dygtige til parallel computing til effektiv databehandling Evne til at samle og transformere data ved hjælp af MapReduce Erfaring med præstationsoptimering til MapReduce -job

MapReduce Online Test

Q: What topics are covered in the MapReduce Online Test?

Mapreduce Big databehandling Distribueret computing Dataanalyse Hadoop Databehandling Parallel computing Dataaggregering Datatransformation Præstationsoptimering

About the test:

Den MapReduce Online Test bruger scenariebaserede MCQ'er til at evaluere kandidater om deres viden om MapReduce-rammer, herunder deres dygtighed i at arbejde med Hadoop, HDFS og garn. Testen evaluerer også en kandidats fortrolighed med Pig og Hive til dataanalyse og deres evne til at arbejde med Big Data Technologies. Testen sigter mod at evaluere en kandidats evne til at designe og udvikle applikationer ved hjælp af MapReduce -rammer og relaterede teknologier effektivt.

Covered skills:

Mapreduce
Distribueret computing
Hadoop
Parallel computing
Datatransformation

Big databehandling
Dataanalyse
Databehandling
Dataaggregering
Præstationsoptimering

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9 reasons why

Adaface MapReduce Test is the most accurate way to shortlist Big Data Developers

Reason #1

Tests for on-the-job skills

The MapReduce Online Test helps recruiters and hiring managers identify qualified candidates from a pool of resumes, and helps in taking objective hiring decisions. It reduces the administrative overhead of interviewing too many candidates and saves time by filtering out unqualified candidates at the first step of the hiring process.

The test screens for the following skills that hiring managers look for in candidates:

Evne til at skrive effektive MapReduce -programmer
Forståelse af big databehandlingsprincipper
Kendskab til distribuerede computerkoncepter
Færdighed i dataanalyseteknikker
Erfaring med Hadoop -rammer
Evne til at behandle store mængder data
Forståelse af parallelle computerprincipper
Færdigheder i dataggregering og opsummering
Færdighed i datatransformation og manipulation
Kendskab til ydelsesoptimeringsteknikker

Reason #2

No trick questions

Traditional assessment tools use trick questions and puzzles for the screening, which creates a lot of frustration among candidates about having to go through irrelevant screening assessments.

View sample questions

The main reason we started Adaface is that traditional pre-employment assessment platforms are not a fair way for companies to evaluate candidates. At Adaface, our mission is to help companies find great candidates by assessing on-the-job skills required for a role.

Why we started Adaface

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Reason #3

Non-googleable questions

We have a very high focus on the quality of questions that test for on-the-job skills. Every question is non-googleable and we have a very high bar for the level of subject matter experts we onboard to create these questions. We have crawlers to check if any of the questions are leaked online. If/ when a question gets leaked, we get an alert. We change the question for you & let you know.

How we design questions

Dette er kun en lille prøve fra vores bibliotek med 10.000+ spørgsmål. De faktiske spørgsmål om dette MapReduce -test vil være ikke-gåbart.

🧐 Question
Easy Count number of occurrences Mappers Reducers	Solve
Chusk works as Hadoop developer at Pesla Inc. Chusk is tasked with processing input data to count number of occurrences of each unique word. Chusk did the following to achieve this: 1. Tokenize each word and emit lateral value 1 with Mapper 2. Reducer increments counter for each literal 1 it receives Chusk is now tasked with optimizing this by using a combiner. Will Chusk be able to reuse existing reducers as combiners? A: Yes B: No C: Because the sum operation is both associative and commutative and the input and output types to the reduce method match D: Because the sum operation in the Reducer is incompatible with the operation of a combiner E: Because the combiner is incompatible with a Mapper, which doesn't use the same data type for both the key and value F: Insufficient information
Medium Hive ngrams	Solve
Assuming the following Hive statements execute successfully, choose the correct statements that describe the result: from fooddata select context_ngrams(sentences(lines), array("twiggy", "romato", null), 68); A. A bigram of the top 68 sentences that contain the substring "twiggy romato" in the lines column of the input data A1 table. B. An 68-value ngram of sentences that contain the words "twiggy" or "romato" in the lines column of the fooddata table. C. A trigram of the top 68 sentences that contain "twiggy romato" followed by a null space in the lines column of the fooddata table. D. A frequency distribution of the top 68 words that follow the subsequence "twiggy romato" in the lines column of the fooddata table.
Easy P Q relations Pig	Solve
Consider the following two relations, P and Q: What is the output of the following Pig command? Q = GROUP P BY p2; DUMP Q;

	🧐 Question	🔧 Skill
	Easy Count number of occurrences Mappers Reducers	3 mins Hadoop	Solve
Chusk works as Hadoop developer at Pesla Inc. Chusk is tasked with processing input data to count number of occurrences of each unique word. Chusk did the following to achieve this: 1. Tokenize each word and emit lateral value 1 with Mapper 2. Reducer increments counter for each literal 1 it receives Chusk is now tasked with optimizing this by using a combiner. Will Chusk be able to reuse existing reducers as combiners? A: Yes B: No C: Because the sum operation is both associative and commutative and the input and output types to the reduce method match D: Because the sum operation in the Reducer is incompatible with the operation of a combiner E: Because the combiner is incompatible with a Mapper, which doesn't use the same data type for both the key and value F: Insufficient information
	Medium Hive ngrams	2 mins Hadoop	Solve
Assuming the following Hive statements execute successfully, choose the correct statements that describe the result: from fooddata select context_ngrams(sentences(lines), array("twiggy", "romato", null), 68); A. A bigram of the top 68 sentences that contain the substring "twiggy romato" in the lines column of the input data A1 table. B. An 68-value ngram of sentences that contain the words "twiggy" or "romato" in the lines column of the fooddata table. C. A trigram of the top 68 sentences that contain "twiggy romato" followed by a null space in the lines column of the fooddata table. D. A frequency distribution of the top 68 words that follow the subsequence "twiggy romato" in the lines column of the fooddata table.
	Easy P Q relations Pig	2 mins Hadoop	Solve
Consider the following two relations, P and Q: What is the output of the following Pig command? Q = GROUP P BY p2; DUMP Q;

	🧐 Question	🔧 Skill	💪 Difficulty	⌛ Time
	Count number of occurrences Mappers Reducers	Hadoop	Easy	3 mins	Solve
Chusk works as Hadoop developer at Pesla Inc. Chusk is tasked with processing input data to count number of occurrences of each unique word. Chusk did the following to achieve this: 1. Tokenize each word and emit lateral value 1 with Mapper 2. Reducer increments counter for each literal 1 it receives Chusk is now tasked with optimizing this by using a combiner. Will Chusk be able to reuse existing reducers as combiners? A: Yes B: No C: Because the sum operation is both associative and commutative and the input and output types to the reduce method match D: Because the sum operation in the Reducer is incompatible with the operation of a combiner E: Because the combiner is incompatible with a Mapper, which doesn't use the same data type for both the key and value F: Insufficient information
	Hive ngrams	Hadoop	Medium	2 mins	Solve
Assuming the following Hive statements execute successfully, choose the correct statements that describe the result: from fooddata select context_ngrams(sentences(lines), array("twiggy", "romato", null), 68); A. A bigram of the top 68 sentences that contain the substring "twiggy romato" in the lines column of the input data A1 table. B. An 68-value ngram of sentences that contain the words "twiggy" or "romato" in the lines column of the fooddata table. C. A trigram of the top 68 sentences that contain "twiggy romato" followed by a null space in the lines column of the fooddata table. D. A frequency distribution of the top 68 words that follow the subsequence "twiggy romato" in the lines column of the fooddata table.
	P Q relations Pig	Hadoop	Easy	2 mins	Solve
Consider the following two relations, P and Q: What is the output of the following Pig command? Q = GROUP P BY p2; DUMP Q;

Reason #4

1200+ customers in 75 countries

Med Adaface var vi i stand til at optimere vores indledende screeningsproces med op mod 75 %, hvilket frigjorde kostbar tid for både ansættelsesledere og vores talentanskaffelsesteam!

Brandon Lee, Leder af mennesker, Love, Bonito

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Reason #5

Designed for elimination, not selection

The most important thing while implementing the pre-employment MapReduce -test in your hiring process is that it is an elimination tool, not a selection tool. In other words: you want to use the test to eliminate the candidates who do poorly on the test, not to select the candidates who come out at the top. While they are super valuable, pre-employment tests do not paint the entire picture of a candidate’s abilities, knowledge, and motivations. Multiple easy questions are more predictive of a candidate's ability than fewer hard questions. Harder questions are often "trick" based questions, which do not provide any meaningful signal about the candidate's skillset.

Science behind Adaface tests

Reason #6

1 click candidate invites

Email invites: You can send candidates an email invite to the MapReduce -test from your dashboard by entering their email address.

Public link: You can create a public link for each test that you can share with candidates.

API or integrations: You can invite candidates directly from your ATS by using our pre-built integrations with popular ATS systems or building a custom integration with your in-house ATS.

Reason #7

Detailed scorecards & benchmarks

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Reason #8

High completion rate

Adaface tests are conversational, low-stress, and take just 25-40 mins to complete.

This is why Adaface has the highest test-completion rate (86%), which is more than 2x better than traditional assessments.

Reason #9

Advanced Proctoring

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About the MapReduce Assessment Test

Why you should use Pre-employment MapReduce Online Test?

The MapReduce -test makes use of scenario-based questions to test for on-the-job skills as opposed to theoretical knowledge, ensuring that candidates who do well on this screening test have the relavant skills. The questions are designed to covered following on-the-job aspects:

Evne til at implementere MapReduce -algoritmer til big -databehandling
Færdighed i Hadoop -økosystem og dets komponenter
Forståelse af distribuerede computerprincipper
Kapacitet til at analysere data ved hjælp af MapReduce -teknikker
Kendskab til Hadoops arkitektur og dens rolle i big databehandling
Ekspertise inden for databehandling ved hjælp af MapReduce -rammer
Dygtige til parallel computing til effektiv databehandling
Evne til at samle og transformere data ved hjælp af MapReduce
Erfaring med præstationsoptimering til MapReduce -job

Once the test is sent to a candidate, the candidate receives a link in email to take the test. For each candidate, you will receive a detailed report with skills breakdown and benchmarks to shortlist the top candidates from your pool.

What topics are covered in the MapReduce Online Test?

big databehandling
Big Data -behandling involverer styring og analyse af Store mængder komplekse data fra forskellige kilder. Det kræver teknikker og værktøjer, såsom MapReduce, for effektivt at behandle og udtrække meningsfuld indsigt fra dataene. Evaluering af kandidaternes færdigheder i big databehandling vil hjælpe rekrutterere med at identificere personer, der kan håndtere de udfordringer, der er relateret til at arbejde med massive datasæt.
Distribueret computing
Distribueret computer refererer til brugen af flere computere at løse et problem eller udføre en opgave. Det giver mulighed for parallel behandling og kan forbedre den samlede ydelse og skalerbarhed markant. Det er vigtigt at måle kandidaternes færdigheder i distribueret computing, da det indikerer deres evne til at designe og implementere skalerbare og effektive løsninger i et distribueret miljø.
dataanalyse
Dataanalyse involverer efterforskningen, transformation og modellering af data for at udtrække værdifuld indsigt og støtte beslutningstagning. Evaluering af kandidaternes færdigheder i dataanalyse gør det muligt for rekrutterere at identificere personer, der effektivt kan analysere og fortolke komplekse datasæt, hvilket giver værdifuld indsigt til at drive forretningsresultater.
hadoop
hadoop er en åben- Kilderamme, der giver et distribueret filsystem og understøtter behandlingen af Big Data ved hjælp af MapReduce -programmeringsmodellen. Evaluering af kandidaternes hadoop -færdigheder er afgørende, da det viser deres dygtighed i at bruge dette kraftfulde værktøj til styring og behandling af store datasæt.
databehandling
Databehandling refererer til manipulation og transformation af data For at udtrække nyttige oplysninger eller forberede dem til yderligere analyse. Evaluering af kandidaternes færdigheder i databehandling sikrer, at de effektivt kan styre og rengøre store datasæt, hvilket forbedrer deres evne til at arbejde med big data effektivt.
parallel computing
Parallel computing involverer at dele et problem til mindre opgaver, der kan udføres samtidig på flere processorer eller computere. Det muliggør hurtigere behandling af komplekse beregninger og er især nyttigt i big -databehandling. Måling af kandidaternes færdigheder i parallel computing hjælper med at identificere individer, der er i stand til at designe og implementere parallelle algoritmer til effektiv databehandling.
dataaggregation
Data -aggregering er processen med at indsamle og opsummere data fra flere Kilder til en enkelt, let håndterbar form. Det spiller en afgørende rolle i big databehandling, da det muliggør effektiv opbevaring og hentning af relevant information. Evaluering af kandidaternes færdigheder inden for datasamling sikrer, at de effektivt kan indsamle og konsolidere data fra forskellige kilder, hvilket understøtter mere avancerede dataanalyseopgaver.
Full list of covered topics
The actual topics of the questions in the final test will depend on your job description and requirements. However, here's a list of topics you can expect the questions for MapReduce -test to be based on.
MapReduce Basics
Mapperfunktion
Reducerfunktion
Kombineringsfunktion
Input- og outputformater
Sekundær sortering
Opdeling og blanding
Tællere i MapReduce
MapReduce joboptimering
Deltag i operationer i MapReduce
Dataserialisering i MapReduce
Hadoop Architecture
HDFS (Hadoop Distribueret filsystem)
Garn (endnu en ressourceforhandler)
Hadoop MapReduce Framework
Hadoop streaming
Hadoop MapReduce jobudførelse
Databehandling i Hadoop
Datalokalitet i Hadoop
Distribueret opbevaring og behandling i Hadoop
Dataanalyseteknikker
Undersøgende dataanalyse
Datarensning og forarbejdning
Statistisk analyse i MapReduce
Arbejder med store datasæt
Datatransformationsteknikker
Dataaggregering og opsummering
Præstationsoptimering i MapReduce
Fejltolerance i distribueret computing
Parallelle computerprincipper
Distribuerede computerrammer
Cluster Computing
Distribueret ressourcestyring
Skalerbarhed i distribueret computing
Data parallelisme
Opgave Parallelisme
Samtidighedskontrol
Dataintegration og konsolidering
Datalagerdesign
Datakvalitetsvurdering
ETL (ekstrakt, transform, belastning)
Datavisualisering
Datalagring
Performance Tuning
Streaming af data
Databehandling af realtid
Massivt parallel behandling
Cloud computing og big data
Data -styring
Datasikkerhed i distribuerede systemer
Dataintegritet og konsistens
Databeskyttelse og overholdelse
Data -sikkerhedskopiering og katastrofegendannelse
Datareplikation

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What roles can I use the MapReduce Online Test for?

Big Data Developer
Hadopp -udvikler
Dataingeniør

How is the MapReduce Online Test customized for senior candidates?

For intermediate/ experienced candidates, we customize the assessment questions to include advanced topics and increase the difficulty level of the questions. This might include adding questions on topics like

Erfaring med databehandling ved hjælp af MapReduce
Fortrolighed med parallel computing
Dataaggregering og transformationsevner
Resultatoptimeringskendskab
Forståelse af big databehandling
Dybdegående viden om distribuerede computerprincipper
Evne til at samle og transformere komplekse data

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Ansættelseslederne mente, at de gennem de tekniske spørgsmål, som de stillede under panelinterviewene, var i stand til at fortælle, hvilke kandidater der havde bedre score og differentieret med dem, der ikke scorede så godt. De er meget tilfreds med kvaliteten af de kandidater, der er nomineret med Adaface-screeningen.

85%

Reduktion i screeningstid

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MapReduce Hiring Test Ofte stillede spørgsmål

Kan jeg kombinere flere færdigheder i en brugerdefineret vurdering?

Ja absolut. Brugerdefinerede vurderinger er oprettet baseret på din jobbeskrivelse og vil omfatte spørgsmål om alle must-have-færdigheder, du angiver.

Har du nogen anti-cheating eller proctoring-funktioner på plads?

Vi har følgende anti-cheating-funktioner på plads:

Ikke-gåbare spørgsmål
IP Proctoring
Webproctoring
Webcam Proctoring
Detektion af plagiering
Sikker browser

Læs mere om Proctoring Features.

Hvordan fortolker jeg testresultater?

Den primære ting at huske på er, at en vurdering er et elimineringsværktøj, ikke et udvælgelsesværktøj. En færdighedsvurdering er optimeret for at hjælpe dig med at eliminere kandidater, der ikke er teknisk kvalificerede til rollen, den er ikke optimeret til at hjælpe dig med at finde den bedste kandidat til rollen. Så den ideelle måde at bruge en vurdering på er at beslutte en tærskelværdi (typisk 55%, vi hjælper dig med benchmark) og inviterer alle kandidater, der scorer over tærsklen for de næste interviewrunder.

Hvilken oplevelsesniveau kan jeg bruge denne test til?

Hver Adaface -vurdering tilpasses til din jobbeskrivelse/ ideel kandidatperson (vores emneeksperter vælger de rigtige spørgsmål til din vurdering fra vores bibliotek på 10000+ spørgsmål). Denne vurdering kan tilpasses til ethvert erfaringsniveau.

Får hver kandidat de samme spørgsmål?

Ja, det gør det meget lettere for dig at sammenligne kandidater. Valgmuligheder for MCQ -spørgsmål og rækkefølgen af spørgsmål randomiseres. Vi har anti-cheating/proctoring funktioner på plads. I vores virksomhedsplan har vi også muligheden for at oprette flere versioner af den samme vurdering med spørgsmål om lignende vanskelighedsniveauer.