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 -koncepter og teknikker Teradata MDM Arkitektur og funktionalitet Datastyring Bedste praksis og metodologier Principper og implementering af datastyring og implementering Master datastyringsteknikker og arbejdsgange Strategier og værktøjer til dataintegration Datakvalitetsvurdering og forbedringsteknikker Datamodelleringsprincipper og teknikker Datalagringskoncepter og design Datasikkerhed og privatlivspraksis i MDM

Teradata MDM Online Test

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

Teradata Mdm Datastyring Data -styring Master Data Management Dataintegration Datakvalitet Datamodellering Datalagring

About the test:

Teradata MDM online test evaluerer en kandidats viden og færdigheder i Teradata Master Data Management (MDM). Den vurderer forståelse af datastyring, datastyring, dataintegration, datakvalitet, datamodellering, datalagring, dataanalyse og datasikkerhed.

Covered skills:

Teradata Mdm
Data -styring
Dataintegration
Datamodellering

Datastyring
Master Data Management
Datakvalitet
Datalagring

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9 reasons why

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

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 at forstå og implementere Teradata MDM -principper og bedste praksis
Evne til at designe og implementere datastyringspolitikker og procedurer
Evne til at integrere data fra forskellige kilder i et Teradata MDM -system
Evne til at sikre datakvalitet og nøjagtighed i et Teradata MDM -miljø
Evne til at modellere og strukturere data i en Teradata MDM -løsning
Evne til at udvikle og vedligeholde datalagringsløsninger ved hjælp af Teradata MDM
Evne til at fejlfinde og løse problemer relateret til Teradata MDM -implementering
Evne til at samarbejde med interessenter til at definere og prioritere masterdatakrav
Evne til at administrere og vedligeholde mastedata inden for et Teradata MDM -system
Evne til at udføre dataanalyse og identificere datamønstre og tendenser 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 kun en lille prøve fra vores bibliotek med 10.000+ spørgsmål. De faktiske spørgsmål om dette Teradata MDM -test vil være ikke-gåbart.

🧐 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 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 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 -koncepter og teknikker
Teradata MDM Arkitektur og funktionalitet
Datastyring Bedste praksis og metodologier
Principper og implementering af datastyring og implementering
Master datastyringsteknikker og arbejdsgange
Strategier og værktøjer til dataintegration
Datakvalitetsvurdering og forbedringsteknikker
Datamodelleringsprincipper og teknikker
Datalagringskoncepter og design
Datasikkerhed og privatlivspraksis 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) henviser til processer, styring og værktøjer, der bruges til at sikre, at en organisations kritiske data er komplette, nøjagtige og ajourførte på tværs af forskellige systemer og applikationer. Denne færdighed måles i testen for at vurdere en kandidats forståelse af, hvordan man implementerer og administrerer Teradata MDM -løsninger, der giver en konsolideret, pålidelig og enkelt visning af masterdata.
datastyring </h4> <P > Datastyring involverer processer, politikker og værktøjer, der bruges til at behandle, gemme og beskytte data i hele dens livscyklus. Denne færdighed måles i testen for at evaluere en kandidats evne til effektivt at administrere og styre dataaktiver, hvilket sikrer datakvalitet, sikkerhed og overholdelse af industristandarder og lovgivningsmæssige krav. </p> <h4> datastyring </H4> <P > Datastyring henviser til den overordnede styring og kontrol af en organisations dataaktiver, herunder datapolitikker, standarder og processer. Denne færdighed måles i testen for at vurdere en kandidats viden om datastyringsrammer, bedste praksis og deres evne til at etablere og håndhæve datastyringspolitikker for at sikre datakvalitet, integritet og konsistens. </p> <H4> Master Data Management
Master Data Management (MDM) er en omfattende tilgang til at identificere, definere og styre en organisations mest kritiske dataprodukter, såsom kunder, produkter og leverandører, for at sikre konsistens og nøjagtighed på tværs af flere systemer og applikationer . Denne færdighed måles i testen for at evaluere en kandidats forståelse af MDM -koncepter, metodologier og teknologier og deres evne til at designe og implementere effektive MDM -løsninger.
dataintegration
Dataintegration involverer at kombinere data fra forskellige kilder og formater i en samlet visning til analyse, rapportering og beslutningstagning. Denne færdighed måles i testen for at vurdere en kandidats viden om dataintegrationsteknikker, værktøjer og teknologier og deres evne til at designe og implementere dataintegrationsløsninger, der muliggør problemfri datastrøm og interoperabilitet mellem forskellige systemer.
Datakvalitet
Datakvalitet refererer til data om nøjagtighed, konsistens, fuldstændighed og pålidelighed. Denne færdighed måles i testen for at evaluere en kandidats forståelse af datakvalitetsvurdering, dataprofilering og datarensningsteknikker samt deres evne til at implementere datakvalitetsrammer og processer for at sikre data af høj kvalitet til pålidelig beslutningstagning og operationel effektivitet .
Datamodellering
Datamodellering er processen med at skabe en konceptuel eller logisk repræsentation af datastrukturer, enheder og relationer for en organisations datasaktiver. Denne færdighed måles i testen for at vurdere en kandidats evne til at designe og udvikle datamodeller, der opfylder forretningskravene, sikrer dataintegritet og letter effektiv datalagring, hentning og manipulation.
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
Principper for datastyring
Politikker og procedurer for datastyring
Dataintegrationsteknikker
Datakvalitetsstyring
Datamodelleringsteknikker
Dimensionel modellering
Normalisering
Stjerneskema
Snowflake -skema
Datalagringskoncepter
ETL (ekstrakt, transform, belastning) processer
Dataprofilering
Datakortlægning
Datarensning
Databerigelse
Data Stewardship
Dataforhold
Data validering
Datamigration
Data Matching
Data deduplikation
Datastandardisering
Datasynkronisering
Værktøjer til styringsstyring
Databeskyttelse og sikkerhed
Datasamarbejde og deling
Dataanalyse
Datavisualisering
Strategier for styring af masterdata
Masterdata -domæner
Master Data Governance
Masterdatamodellering
Master dataintegration
Master datakvalitet
Master Data Governance Tools
Teradata MDM -administration
Teradata MDM -konfiguration
Teradata MDM -implementering
Teradata MDM Fejlfinding
Teradata MDM Security
Teradata MDM Performance Tuning
Teradata MDM skalerbarhed
Teradata MDM Data Migration
Teradata MDM -datasynkronisering
Teradata MDM -datamodellering
Teradata MDM -datavalidering
Teradata MDM Data Lineage
Teradata MDM Data Governance
Teradata MDM Data Integration
Teradata MDM -datakvalitet

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

Teradata MDM -udvikler
Sr. Teradata MDM -udvikler
SR MDM -udvikler
MDM -applikationer

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

Dybdegående viden om Teradata MDM-funktionaliteter og funktioner
Evne til at designe og implementere komplekse datatransformationsscenarier
Erfaring med datastyringsrammer og politikker
Dygtige til at bruge ETL -værktøjer til dataintegration
Stærk forståelse af profilering af datakvalitet og rensningsteknikker
Ekspertise inden for dimensionel modellering og skema design
Kendskab til ETL -planlægning og overvågningsprocesser
Erfaring med at opbygge skalerbare og effektive datalager
Forståelse af regler for databeskyttelse og overholdelse
Dygtige til fejlfinding af MDM -relaterede problemer

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Se prøvescorekort

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 sig 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

Se den fulde casestudie

Teradata MDM 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.