Why you should use Teradata MDM Online Test?

The Teradata MDM -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: ETL -konsepter og teknikker Teradata MDM -arkitektur og funksjonalitet Datastyring Beste praksis og metodologier Prinsipper og implementering av datastyring og implementering Master Data Management -teknikker og arbeidsflyter Dataintegrasjonsstrategier og verktøy Datakvalitetsvurdering og forbedringsteknikker Datamodelleringsprinsipper og teknikker Datavarehuskonsepter og design Datasikkerhet og personvernpraksis i MDM

Teradata MDM Online Test

Q: What topics are covered in the Teradata MDM Online Test?

Teradata Mdm Dataledelse Datastyring Master Data Management Dataintegrasjon Datakvalitet Datamodellering Datavarehus

About the test:

Teradata MDM Online Test evaluerer en kandidats kunnskap og ferdigheter i Teradata Master Data Management (MDM). Den vurderer forståelse av datahåndtering, datastyring, dataintegrasjon, datakvalitet, datamodellering, datavarehus, dataanalyse og datasikkerhet.

Covered skills:

Teradata Mdm
Datastyring
Dataintegrasjon
Datamodellering

Dataledelse
Master Data Management
Datakvalitet
Datavarehus

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9 reasons why

Adaface Teradata MDM Test is the most accurate way to shortlist Teradata MDM -utviklers

Reason #1

Tests for on-the-job skills

The Teradata MDM 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 å forstå og implementere Teradata MDM -prinsipper og beste praksis
Evne til å designe og implementere retningslinjer og prosedyrer for datastyring
Evne til å integrere data fra forskjellige kilder i et Teradata MDM -system
Evne til å sikre datakvalitet og nøyaktighet i et Teradata MDM -miljø
Evne til å modellere og strukturere data i en Teradata MDM -løsning
Evne til å utvikle og vedlikeholde datavarehusløsninger ved hjelp av Teradata MDM
Evne til å feilsøke og løse problemer relatert til Teradata MDM -implementering
Evne til å samarbeide med interessenter til å definere og prioritere krav til masterdata
Evne til å administrere og vedlikeholde masterdata i et Teradata MDM -system
Evne til å utføre dataanalyse og identifisere datamønstre og trender i et Teradata MDM -miljø

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 bare en liten prøve fra biblioteket vårt med 10.000+ spørsmål. De faktiske spørsmålene om dette Teradata MDM -test vil være ikke-googlable.

🧐 Question
Medium Data Merging Data Merging Conditional Logic	Try practice test
A data engineer is tasked with merging and transforming data from two sources for a business analytics report. Source 1 is a SQL database 'Employee' with fields EmployeeID (int), Name (varchar), DepartmentID (int), and JoinDate (date). Source 2 is a CSV file 'Department' with fields DepartmentID (int), DepartmentName (varchar), and Budget (float). The objective is to create a summary table that lists EmployeeID, Name, DepartmentName, and YearsInCompany. The YearsInCompany should be calculated based on the JoinDate and the current date, rounded down to the nearest whole number. Consider the following initial SQL query: Which of the following modifications ensures accurate data transformation as per the requirements? A: Change FLOOR to CEILING in the calculation of YearsInCompany. B: Add WHERE e.JoinDate IS NOT NULL before the JOIN clause. C: Replace JOIN with LEFT JOIN and use COALESCE(d.DepartmentName, 'Unknown'). D: Change the YearsInCompany calculation to YEAR(CURRENT_DATE) - YEAR(e.JoinDate). E: Use DATEDIFF(YEAR, e.JoinDate, CURRENT_DATE) for YearsInCompany calculation.
Medium Data Updates Staging Data Warehouse	Try practice test
Jaylo is hired as Data warehouse engineer at Affflex Inc. Jaylo is tasked with designing an ETL process for loading data from SQL server database into a large fact table. Here are the specifications of the system: 1. Orders data from SQL to be stored in fact table in the warehouse each day with prior day’s order data 2. Loading new data must take as less time as possible 3. Remove data that is more then 2 years old 4. Ensure the data loads correctly 5. Minimize record locking and impact on transaction log Which of the following should be part of Jaylo’s ETL design? A: Partition the destination fact table by date B: Partition the destination fact table by customer C: Insert new data directly into fact table D: Delete old data directly from fact table E: Use partition switching and staging table to load new data F: Use partition switching and staging table to remove old data
Medium SQL in ETL Process SQL Code Interpretation Data Transformation SQL Functions	Try practice test
In an ETL process designed for a retail company, a complex SQL transformation is applied to the 'Sales' table. The 'Sales' table has fields SaleID, ProductID, Quantity, SaleDate, and Price. The goal is to generate a report that shows the total sales amount and average sale amount per product, aggregated monthly. The following SQL code snippet is used in the transformation step: What specific function does this SQL code perform in the context of the ETL process, and how does it contribute to the reporting goal? A: The code calculates the total and average sales amount for each product annually. B: It aggregates sales data by month and product, computing total and average sales amounts. C: This query generates a daily breakdown of sales, both total and average, for each product. D: The code is designed to identify the best-selling products on a monthly basis by sales amount. E: It calculates the overall sales and average price per product, without considering the time dimension.
Medium Trade Index Index	Try practice test
Silverman Sachs is a trading firm and deals with daily trade data for various stocks. They have the following fact table in their data warehouse: Table: Trades Indexes: None Columns: TradeID, TradeDate, Open, Close, High, Low, Volume Here are three common queries that are run on the data: Dhavid Polomon is hired as an ETL Developer and is tasked with implementing an indexing strategy for the Trades fact table. Here are the specifications of the indexing strategy: - All three common queries must use a columnstore index - Minimize number of indexes - Minimize size of indexes Which of the following strategies should Dhavid pick: A: Create three columnstore indexes: 1. Containing TradeDate and Close 2. Containing TradeDate, High and Low 3. Container TradeDate and Volume B: Create two columnstore indexes: 1. Containing TradeID, TradeDate, Volume and Close 2. Containing TradeID, TradeDate, High and Low C: Create one columnstore index that contains TradeDate, Close, High, Low and Volume D: Create one columnstore index that contains TradeID, Close, High, Low, Volume and Trade Date

	🧐 Question	🔧 Skill
	Medium Data Merging Data Merging Conditional Logic	2 mins ETL	Try practice test
A data engineer is tasked with merging and transforming data from two sources for a business analytics report. Source 1 is a SQL database 'Employee' with fields EmployeeID (int), Name (varchar), DepartmentID (int), and JoinDate (date). Source 2 is a CSV file 'Department' with fields DepartmentID (int), DepartmentName (varchar), and Budget (float). The objective is to create a summary table that lists EmployeeID, Name, DepartmentName, and YearsInCompany. The YearsInCompany should be calculated based on the JoinDate and the current date, rounded down to the nearest whole number. Consider the following initial SQL query: Which of the following modifications ensures accurate data transformation as per the requirements? A: Change FLOOR to CEILING in the calculation of YearsInCompany. B: Add WHERE e.JoinDate IS NOT NULL before the JOIN clause. C: Replace JOIN with LEFT JOIN and use COALESCE(d.DepartmentName, 'Unknown'). D: Change the YearsInCompany calculation to YEAR(CURRENT_DATE) - YEAR(e.JoinDate). E: Use DATEDIFF(YEAR, e.JoinDate, CURRENT_DATE) for YearsInCompany calculation.
	Medium Data Updates Staging Data Warehouse	2 mins ETL	Try practice test
Jaylo is hired as Data warehouse engineer at Affflex Inc. Jaylo is tasked with designing an ETL process for loading data from SQL server database into a large fact table. Here are the specifications of the system: 1. Orders data from SQL to be stored in fact table in the warehouse each day with prior day’s order data 2. Loading new data must take as less time as possible 3. Remove data that is more then 2 years old 4. Ensure the data loads correctly 5. Minimize record locking and impact on transaction log Which of the following should be part of Jaylo’s ETL design? A: Partition the destination fact table by date B: Partition the destination fact table by customer C: Insert new data directly into fact table D: Delete old data directly from fact table E: Use partition switching and staging table to load new data F: Use partition switching and staging table to remove old data
	Medium SQL in ETL Process SQL Code Interpretation Data Transformation SQL Functions	3 mins ETL	Try practice test
In an ETL process designed for a retail company, a complex SQL transformation is applied to the 'Sales' table. The 'Sales' table has fields SaleID, ProductID, Quantity, SaleDate, and Price. The goal is to generate a report that shows the total sales amount and average sale amount per product, aggregated monthly. The following SQL code snippet is used in the transformation step: What specific function does this SQL code perform in the context of the ETL process, and how does it contribute to the reporting goal? A: The code calculates the total and average sales amount for each product annually. B: It aggregates sales data by month and product, computing total and average sales amounts. C: This query generates a daily breakdown of sales, both total and average, for each product. D: The code is designed to identify the best-selling products on a monthly basis by sales amount. E: It calculates the overall sales and average price per product, without considering the time dimension.
	Medium Trade Index Index	3 mins ETL	Try practice test
Silverman Sachs is a trading firm and deals with daily trade data for various stocks. They have the following fact table in their data warehouse: Table: Trades Indexes: None Columns: TradeID, TradeDate, Open, Close, High, Low, Volume Here are three common queries that are run on the data: Dhavid Polomon is hired as an ETL Developer and is tasked with implementing an indexing strategy for the Trades fact table. Here are the specifications of the indexing strategy: - All three common queries must use a columnstore index - Minimize number of indexes - Minimize size of indexes Which of the following strategies should Dhavid pick: A: Create three columnstore indexes: 1. Containing TradeDate and Close 2. Containing TradeDate, High and Low 3. Container TradeDate and Volume B: Create two columnstore indexes: 1. Containing TradeID, TradeDate, Volume and Close 2. Containing TradeID, TradeDate, High and Low C: Create one columnstore index that contains TradeDate, Close, High, Low and Volume D: Create one columnstore index that contains TradeID, Close, High, Low, Volume and Trade Date

	🧐 Question	🔧 Skill	💪 Difficulty	⌛ Time
	Data Merging Data Merging Conditional Logic	ETL	Medium	2 mins	Try practice test
A data engineer is tasked with merging and transforming data from two sources for a business analytics report. Source 1 is a SQL database 'Employee' with fields EmployeeID (int), Name (varchar), DepartmentID (int), and JoinDate (date). Source 2 is a CSV file 'Department' with fields DepartmentID (int), DepartmentName (varchar), and Budget (float). The objective is to create a summary table that lists EmployeeID, Name, DepartmentName, and YearsInCompany. The YearsInCompany should be calculated based on the JoinDate and the current date, rounded down to the nearest whole number. Consider the following initial SQL query: Which of the following modifications ensures accurate data transformation as per the requirements? A: Change FLOOR to CEILING in the calculation of YearsInCompany. B: Add WHERE e.JoinDate IS NOT NULL before the JOIN clause. C: Replace JOIN with LEFT JOIN and use COALESCE(d.DepartmentName, 'Unknown'). D: Change the YearsInCompany calculation to YEAR(CURRENT_DATE) - YEAR(e.JoinDate). E: Use DATEDIFF(YEAR, e.JoinDate, CURRENT_DATE) for YearsInCompany calculation.
	Data Updates Staging Data Warehouse	ETL	Medium	2 mins	Try practice test
Jaylo is hired as Data warehouse engineer at Affflex Inc. Jaylo is tasked with designing an ETL process for loading data from SQL server database into a large fact table. Here are the specifications of the system: 1. Orders data from SQL to be stored in fact table in the warehouse each day with prior day’s order data 2. Loading new data must take as less time as possible 3. Remove data that is more then 2 years old 4. Ensure the data loads correctly 5. Minimize record locking and impact on transaction log Which of the following should be part of Jaylo’s ETL design? A: Partition the destination fact table by date B: Partition the destination fact table by customer C: Insert new data directly into fact table D: Delete old data directly from fact table E: Use partition switching and staging table to load new data F: Use partition switching and staging table to remove old data
	SQL in ETL Process SQL Code Interpretation Data Transformation SQL Functions	ETL	Medium	3 mins	Try practice test
In an ETL process designed for a retail company, a complex SQL transformation is applied to the 'Sales' table. The 'Sales' table has fields SaleID, ProductID, Quantity, SaleDate, and Price. The goal is to generate a report that shows the total sales amount and average sale amount per product, aggregated monthly. The following SQL code snippet is used in the transformation step: What specific function does this SQL code perform in the context of the ETL process, and how does it contribute to the reporting goal? A: The code calculates the total and average sales amount for each product annually. B: It aggregates sales data by month and product, computing total and average sales amounts. C: This query generates a daily breakdown of sales, both total and average, for each product. D: The code is designed to identify the best-selling products on a monthly basis by sales amount. E: It calculates the overall sales and average price per product, without considering the time dimension.
	Trade Index Index	ETL	Medium	3 mins	Try practice test
Silverman Sachs is a trading firm and deals with daily trade data for various stocks. They have the following fact table in their data warehouse: Table: Trades Indexes: None Columns: TradeID, TradeDate, Open, Close, High, Low, Volume Here are three common queries that are run on the data: Dhavid Polomon is hired as an ETL Developer and is tasked with implementing an indexing strategy for the Trades fact table. Here are the specifications of the indexing strategy: - All three common queries must use a columnstore index - Minimize number of indexes - Minimize size of indexes Which of the following strategies should Dhavid pick: A: Create three columnstore indexes: 1. Containing TradeDate and Close 2. Containing TradeDate, High and Low 3. Container TradeDate and Volume B: Create two columnstore indexes: 1. Containing TradeID, TradeDate, Volume and Close 2. Containing TradeID, TradeDate, High and Low C: Create one columnstore index that contains TradeDate, Close, High, Low and Volume D: Create one columnstore index that contains TradeID, Close, High, Low, Volume and Trade Date

Reason #4

1200+ customers in 75 countries

Med Adaface var vi i stand til å optimalisere den første screeningsprosessen vår med opp mot 75 %, og frigjorde dyrebar tid for både ansettelsesledere og vårt talentanskaffelsesteam!

Brandon Lee, Leder for mennesker, Love, Bonito

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Reason #5

Designed for elimination, not selection

The most important thing while implementing the pre-employment Teradata MDM -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 Teradata MDM -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

Learn more

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About the Teradata MDM Assessment Test

Why you should use Pre-employment Teradata MDM Online Test?

The Teradata MDM -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:

ETL -konsepter og teknikker
Teradata MDM -arkitektur og funksjonalitet
Datastyring Beste praksis og metodologier
Prinsipper og implementering av datastyring og implementering
Master Data Management -teknikker og arbeidsflyter
Dataintegrasjonsstrategier og verktøy
Datakvalitetsvurdering og forbedringsteknikker
Datamodelleringsprinsipper og teknikker
Datavarehuskonsepter og design
Datasikkerhet og personvernpraksis i MDM

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 Teradata MDM Online Test?

Teradata MDM
Teradata MDM (Master Data Management) refererer til prosesser, styring og verktøy som brukes for å sikre at en organisasjons kritiske data er komplette, nøyaktige og oppdaterte på tvers av forskjellige systemer og applikasjoner. Denne ferdigheten måles i testen for å vurdere en kandidats forståelse av hvordan man implementerer og administrerer Teradata MDM -løsninger som gir en konsolidert, pålitelig og enkelt visning av mesterdata.
Datahåndtering </h4> <P > Datahåndtering involverer prosesser, retningslinjer og verktøy som brukes til å behandle, lagre og beskytte data gjennom hele livssyklusen. Denne ferdigheten måles i testen for å evaluere en kandidats evne til effektivt å forvalte og styre data eiendeler, sikre datakvalitet, sikkerhet og overholdelse av bransjestandarder og myndighetskrav. </p> <h4> Datastyring </h4> <p > Datastyring refererer til den overordnede styringen og kontrollen av en organisasjons datafordeler, inkludert datapolitikk, standarder og prosesser. Denne ferdigheten måles i testen for å vurdere en kandidats kunnskap om rammer for datastyring, beste praksis og deres evne til å etablere og håndheve retningslinjer for datastyring for å sikre datakvalitet, integritet og konsistens. </p> <h4> Master Data Management
Master Data Management (MDM) er en omfattende tilnærming for å identifisere, definere og administrere en organisasjons mest kritiske dataenheter, for eksempel kunder, produkter og leverandører, for å sikre konsistens og nøyaktighet på tvers av flere systemer og applikasjoner . Denne ferdigheten måles i testen for å evaluere en kandidats forståelse av MDM -konsepter, metodologier og teknologier, og deres evne til å designe og implementere effektive MDM -løsninger.
Dataintegrasjon
Dataintegrasjon. innebærer å kombinere data fra forskjellige kilder og formater til et enhetlig syn for analyse, rapportering og beslutningstaking. Denne ferdigheten måles i testen for å vurdere en kandidats kunnskap om dataintegrasjonsteknikker, verktøy og teknologier, og deres evne til å designe og implementere dataintegrasjonsløsninger som muliggjør sømløs dataflyt og interoperabilitet mellom forskjellige systemer.
Datakvalitet
Datakvalitet refererer til nøyaktighet, konsistens, fullstendighet og pålitelighet av data. Denne ferdigheten måles i testen for å evaluere en kandidats forståelse av datakvalitetsvurdering, dataprofilering og datarensingsteknikker, samt deres evne til å implementere datakvalitetsrammer og prosesser for å sikre data av høy kvalitet for pålitelige beslutninger og driftseffektivitet .
Datamodellering
Datamodellering er prosessen med å skape en konseptuell eller logisk representasjon av datastrukturer, enheter og forhold for en organisasjons datamangel. Denne ferdigheten måles i testen for å vurdere en kandidats evne til å designe og utvikle datamodeller som oppfyller forretningskravene, sikrer dataintegritet og letter effektiv datalagring, gjenfinning og manipulering.
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 Teradata MDM -test to be based on.
Teradata MDM -arkitektur
Prinsipper for datastyring
Retningslinjer og prosedyrer for datastyring
Dataintegrasjonsteknikker
Datakvalitetsstyring
Datamodelleringsteknikker
Dimensjonell modellering
Normalisering
Stjernerskjema
Snøfnuggskjema
Datalagerkonsepter
ETL (Extract, Transform, Load) prosesser
Dataprofilering
Datakartlegging
Data rensing
Dataanriking
Data Stewardship
Data avstamning
Datavalidering
Data migrasjon
Datamatching
Data deduplication
Datastandardisering
Datasynkronisering
Verktøy for datastyring
Data personvern og sikkerhet
Datasamarbeid og deling
Dataanalyse
Datavisualisering
Master Data Management Strategies
Master Data Domains
Master Data Governance
Master Data Modelling
Master Data Integration
Master -datakvalitet
Master Data Governance Tools
Teradata MDM -administrasjon
Teradata MDM -konfigurasjon
Teradata MDM -distribusjon
Teradata MDM feilsøking
Teradata MDM Security
Teradata MDM Performance Tuning
Teradata MDM skalerbarhet
Teradata MDM Data Migration
Teradata MDM -datasynkronisering
Teradata MDM -datamodellering
Teradata MDM -datavalidering
Teradata MDM Data Lineage
Teradata MDM Data Styring
Teradata MDM dataintegrasjon
Teradata MDM -datakvalitet

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What roles can I use the Teradata MDM Online Test for?

Teradata MDM -utvikler
Sr. Teradata MDM Developer
SR MDM -utvikler
MDM -applikasjoner

How is the Teradata MDM 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

Dybder kunnskap om Teradata MDM-funksjonaliteter og funksjoner
Evne til å designe og implementere komplekse datatransformasjonsscenarier
Erfaring med rammer for datastyring og retningslinjer
Dyktig til å bruke ETL -verktøy for dataintegrasjon
Sterk forståelse av datakvalitetsprofilering og renseteknikker
Kompetanse innen dimensjonell modellering og skjemadesign
Kunnskap om ETL -planleggings- og overvåkningsprosesser
Erfaring med å bygge skalerbare og effektive datavarehus
Forståelse av datapersonvernforskrifter og etterlevelse
Dyktig til å feilsøke MDM -relaterte problemer

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Ansettelseslederne mente at de gjennom de tekniske spørsmålene de stilte under panelintervjuene, var i stand til å fortelle hvilke kandidater som scoret bedre, og differensierte med de som ikke skåret like godt. De er svært fornøyd med kvaliteten på kandidatene som er på listen med Adaface-screeningen.

85%

Reduksjon i screeningstid

Se hele casestudien

Teradata MDM Hiring Test Vanlige spørsmål

Kan jeg kombinere flere ferdigheter til en tilpasset vurdering?

Ja absolutt. Tilpassede vurderinger er satt opp basert på stillingsbeskrivelsen din, og vil inneholde spørsmål om alle må-ha ferdigheter du spesifiserer.

Har du noen anti-juksende eller proktoreringsfunksjoner på plass?

Vi har følgende anti-juksede funksjoner på plass:

Ikke-googlable spørsmål
IP Proctoring
Nettproctoring
Webcam Proctoring
Deteksjon av plagiering
Sikker nettleser

Les mer om Proctoring -funksjonene.

Hvordan tolker jeg testresultater?

Den viktigste tingen å huske på er at en vurdering er et eliminasjonsverktøy, ikke et seleksjonsverktøy. En ferdighetsvurdering er optimalisert for å hjelpe deg med å eliminere kandidater som ikke er teknisk kvalifisert for rollen, det er ikke optimalisert for å hjelpe deg med å finne den beste kandidaten for rollen. Så den ideelle måten å bruke en vurdering på er å bestemme en terskelpoeng (vanligvis 55%, vi hjelper deg med å benchmark) og invitere alle kandidater som scorer over terskelen for de neste rundene med intervjuet.

Hvilken opplevelsesnivå kan jeg bruke denne testen til?

Hver ADAFACE -vurdering er tilpasset din stillingsbeskrivelse/ ideell kandidatperson (våre fageksperter vil velge de riktige spørsmålene for din vurdering fra vårt bibliotek med 10000+ spørsmål). Denne vurderingen kan tilpasses for ethvert opplevelsesnivå.

Får hver kandidat de samme spørsmålene?

Ja, det gjør det mye lettere for deg å sammenligne kandidater. Alternativer for MCQ -spørsmål og rekkefølgen på spørsmål er randomisert. Vi har anti-juksing/proctoring funksjoner på plass. I vår bedriftsplan har vi også muligheten til å lage flere versjoner av den samme vurderingen med spørsmål med lignende vanskelighetsnivåer.