Why you should use Data Wrangling Test?

The Data Wrangling 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: Dataekstraktion ved hjælp af forskellige metoder Data parsing med præcision og nøjagtighed Dataintegration for problemfri dataflow Kvalitetsvurdering for at sikre dataintegritet Datavalidering for at identificere og korrigere fejl Datamodellering til effektiv repræsentation Datatolkning for at udlede indsigt Dataanalyse ved hjælp af statistiske teknikker Håndtering af undtagelser og fejl i datamanipulation Effektiv styring af store datasæt

Data Wrangling Test

Q: What topics are covered in the Data Wrangling Test?

Dataekstraktion Data parsing Dataintegration Kvalitetsvurdering Data validering Datamodellering Datatolkning Dataanalyse

About the test:

Data Wrangling Test evaluerer en kandidats færdigheder i rengøring, omdannelse og organisering af rå data til et struktureret format, der er egnet til analyse. Det tester deres forståelse af datamodellering, fortolkning, analyse og indgangsprocesser.

Covered skills:

Dataekstraktion
Dataintegration
Data validering
Datatolkning

Data parsing
Kvalitetsvurdering
Datamodellering
Dataanalyse

Preview this test

Se prøvescorekort

9 reasons why

Adaface Data Wrangling Assessment Test is the most accurate way to shortlist Dataanalytikers

Reason #1

Tests for on-the-job skills

The Data Wrangling 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 udtrække data fra forskellige kilder
Evne til at analysere og rengøre data
Evne til at integrere og kombinere data fra flere kilder
Evne til at vurdere kvaliteten af data
Evne til at validere data og identificere eventuelle uoverensstemmelser
Evne til at oprette datamodeller
Evne til at fortolke data og drage meningsfulde konklusioner
Evne til at analysere data ved hjælp af statistiske metoder og værktøjer

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

Preview this test

Se prøvescorekort

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 Data Wrangling Test vil være ikke-gåbart.

🧐 Question
Easy Healthcare System Data Integrity Normalization Referential Integrity	Solve
You are designing a data model for a healthcare system with the following requirements: A: A separate table for each entity with foreign keys as specified, and a DoctorPatient table linking Doctors to Patients. B: A separate table for each entity with foreign keys as specified, without additional tables. C: A combined PatientDoctor table replacing Patient and Doctor, and separate tables for Appointment and Prescription. D: A separate table for each entity with foreign keys, and a PatientPrescription table to track prescriptions directly linked to patients. E: A single table combining Patient, Doctor, Appointment, and Prescription into one. F: A separate table for each entity with foreign keys as specified, and an AppointmentDetails table linking Appointments to Prescriptions.
Hard ER Diagram and minimum tables ER Diagram	Solve
Look at the given ER diagram. What do you think is the least number of tables we would need to represent M, N, P, R1 and R2?
Medium Normalization Process Normalization Database Design Anomaly Elimination	Solve
Consider a healthcare database with a table named PatientRecords that stores patient visit information. The table has the following attributes: - VisitID - PatientID - PatientName - DoctorID - DoctorName - VisitDate - Diagnosis - Treatment - TreatmentCost In this table: - Each VisitID uniquely identifies a patient's visit and is associated with one PatientID. - PatientID is associated with exactly one PatientName. - Each DoctorID is associated with a unique DoctorName. - TreatmentCost is a fixed cost based on the Treatment. Evaluating the PatientRecords table, which of the following statements most accurately describes its normalization state and the required actions for higher normalization? A: The table is in 1NF. To achieve 2NF, remove partial dependencies by separating Patient information (PatientID, PatientName) and Doctor information (DoctorID, DoctorName) into different tables. B: The table is in 2NF. To achieve 3NF, remove transitive dependencies by creating separate tables for Patients (PatientID, PatientName), Doctors (DoctorID, DoctorName), and Visits (VisitID, PatientID, DoctorID, VisitDate, Diagnosis, Treatment, TreatmentCost). C: The table is in 3NF. To achieve BCNF, adjust for functional dependencies such as moving DoctorName to a separate Doctors table. D: The table is in 1NF. To achieve 3NF, create separate tables for Patients, Doctors, and Visits, and remove TreatmentCost as it is a derived attribute. E: The table is in 2NF. To achieve 4NF, address any multi-valued dependencies by separating Visit details and Treatment details. F: The table is in 3NF. To achieve 4NF, remove multi-valued dependencies related to VisitID.
Medium University Courses ER Diagrams Complex Relationships Integrity Constraints	Solve
Based on the ER diagram, which of the following statements is accurate and requires specific knowledge of the ER diagram's details? A: A Student can major in multiple Departments. B: An Instructor can belong to multiple Departments. C: A Course can be offered by multiple Departments. D: Enrollment records can link a Student to multiple Courses in a single semester. E: Each Course must be associated with an Enrollment record. F: A Department can offer courses without having any instructors.
Medium Dividends Financial Analysis Percentage and Average Calculations	Solve
Consider the following line chart which shows the money invested by a company in production each year and the sales made by the company each year. If the pie chart shows the shareholding pattern of the company and the company gives 10% of the profit as dividends to its share holders then what is the average dividend received by retail investors from 2000 to 2004?
Medium Laptop Brands Proportions and Percentages Financial Reasoning	Solve
Given below is the list of laptop brands and their details in which some data is missing. If the cost price of Dell is 3/5 of the cost price of Lenovo, then what will be the %profit of Dell?
Hard Median Trend Analysis Statistical Reasoning	Solve
Consider the following line chart which shows the sales of five different companies from 2000 to 2009. Which of the following companies has the maximum percentage increase in the median from 2000 to 2004 and 2005 to 2009.
Medium Hiring Developer Skewed Data Graph	Solve
Two companies A and B hired developers from the year 2001 to 2005. The given bar graph shows the hiring details. Now select the statements that are true based on the given details. A: The data given for Company A is skewed to the left. B: The data given for Company B is skewed to the right. C: The data given for Company A is skewed to the right. D: For Company B, mean and mode are equal. E: For Company B, mean is equal to median but less than mode. F: For Company A, median is less than mode but greater than mean.
Medium Negative correlation	Solve
Saffi, one of the popular schools in San Francisco did a school wide study of the students in middle school. The study found that there is a negative correlation between the time spent on Facebook per day by students and their academic achievement. How can we understand the results of this study? A: An increase in time spent on Facebook per day causes a drop in the academic achievement of students at the middle school level. B: There is an association between an increase in time spent on Facebook per day and the drop in the academic achievement of students at Saffi. C: An increase in the time spent on Facebook per day causes a drop in the academic achievement of students at Saffi. D: There is an association between an increase in time spent on Facebook per day and the drop in the academic achievement of students at the middle school level.

	🧐 Question	🔧 Skill
	Easy Healthcare System Data Integrity Normalization Referential Integrity	2 mins Data Modeling	Solve
You are designing a data model for a healthcare system with the following requirements: A: A separate table for each entity with foreign keys as specified, and a DoctorPatient table linking Doctors to Patients. B: A separate table for each entity with foreign keys as specified, without additional tables. C: A combined PatientDoctor table replacing Patient and Doctor, and separate tables for Appointment and Prescription. D: A separate table for each entity with foreign keys, and a PatientPrescription table to track prescriptions directly linked to patients. E: A single table combining Patient, Doctor, Appointment, and Prescription into one. F: A separate table for each entity with foreign keys as specified, and an AppointmentDetails table linking Appointments to Prescriptions.
	Hard ER Diagram and minimum tables ER Diagram	2 mins Data Modeling	Solve
Look at the given ER diagram. What do you think is the least number of tables we would need to represent M, N, P, R1 and R2?
	Medium Normalization Process Normalization Database Design Anomaly Elimination	3 mins Data Modeling	Solve
Consider a healthcare database with a table named PatientRecords that stores patient visit information. The table has the following attributes: - VisitID - PatientID - PatientName - DoctorID - DoctorName - VisitDate - Diagnosis - Treatment - TreatmentCost In this table: - Each VisitID uniquely identifies a patient's visit and is associated with one PatientID. - PatientID is associated with exactly one PatientName. - Each DoctorID is associated with a unique DoctorName. - TreatmentCost is a fixed cost based on the Treatment. Evaluating the PatientRecords table, which of the following statements most accurately describes its normalization state and the required actions for higher normalization? A: The table is in 1NF. To achieve 2NF, remove partial dependencies by separating Patient information (PatientID, PatientName) and Doctor information (DoctorID, DoctorName) into different tables. B: The table is in 2NF. To achieve 3NF, remove transitive dependencies by creating separate tables for Patients (PatientID, PatientName), Doctors (DoctorID, DoctorName), and Visits (VisitID, PatientID, DoctorID, VisitDate, Diagnosis, Treatment, TreatmentCost). C: The table is in 3NF. To achieve BCNF, adjust for functional dependencies such as moving DoctorName to a separate Doctors table. D: The table is in 1NF. To achieve 3NF, create separate tables for Patients, Doctors, and Visits, and remove TreatmentCost as it is a derived attribute. E: The table is in 2NF. To achieve 4NF, address any multi-valued dependencies by separating Visit details and Treatment details. F: The table is in 3NF. To achieve 4NF, remove multi-valued dependencies related to VisitID.
	Medium University Courses ER Diagrams Complex Relationships Integrity Constraints	2 mins Data Modeling	Solve
Based on the ER diagram, which of the following statements is accurate and requires specific knowledge of the ER diagram's details? A: A Student can major in multiple Departments. B: An Instructor can belong to multiple Departments. C: A Course can be offered by multiple Departments. D: Enrollment records can link a Student to multiple Courses in a single semester. E: Each Course must be associated with an Enrollment record. F: A Department can offer courses without having any instructors.
	Medium Dividends Financial Analysis Percentage and Average Calculations	3 mins Data Interpretation	Solve
Consider the following line chart which shows the money invested by a company in production each year and the sales made by the company each year. If the pie chart shows the shareholding pattern of the company and the company gives 10% of the profit as dividends to its share holders then what is the average dividend received by retail investors from 2000 to 2004?
	Medium Laptop Brands Proportions and Percentages Financial Reasoning	2 mins Data Interpretation	Solve
Given below is the list of laptop brands and their details in which some data is missing. If the cost price of Dell is 3/5 of the cost price of Lenovo, then what will be the %profit of Dell?
	Hard Median Trend Analysis Statistical Reasoning	3 mins Data Interpretation	Solve
Consider the following line chart which shows the sales of five different companies from 2000 to 2009. Which of the following companies has the maximum percentage increase in the median from 2000 to 2004 and 2005 to 2009.
	Medium Hiring Developer Skewed Data Graph	3 mins Data Analysis	Solve
Two companies A and B hired developers from the year 2001 to 2005. The given bar graph shows the hiring details. Now select the statements that are true based on the given details. A: The data given for Company A is skewed to the left. B: The data given for Company B is skewed to the right. C: The data given for Company A is skewed to the right. D: For Company B, mean and mode are equal. E: For Company B, mean is equal to median but less than mode. F: For Company A, median is less than mode but greater than mean.
	Medium Negative correlation	2 mins Data Analysis	Solve
Saffi, one of the popular schools in San Francisco did a school wide study of the students in middle school. The study found that there is a negative correlation between the time spent on Facebook per day by students and their academic achievement. How can we understand the results of this study? A: An increase in time spent on Facebook per day causes a drop in the academic achievement of students at the middle school level. B: There is an association between an increase in time spent on Facebook per day and the drop in the academic achievement of students at Saffi. C: An increase in the time spent on Facebook per day causes a drop in the academic achievement of students at Saffi. D: There is an association between an increase in time spent on Facebook per day and the drop in the academic achievement of students at the middle school level.

	🧐 Question	🔧 Skill	💪 Difficulty	⌛ Time
	Healthcare System Data Integrity Normalization Referential Integrity	Data Modeling	Easy	2 mins	Solve
You are designing a data model for a healthcare system with the following requirements: A: A separate table for each entity with foreign keys as specified, and a DoctorPatient table linking Doctors to Patients. B: A separate table for each entity with foreign keys as specified, without additional tables. C: A combined PatientDoctor table replacing Patient and Doctor, and separate tables for Appointment and Prescription. D: A separate table for each entity with foreign keys, and a PatientPrescription table to track prescriptions directly linked to patients. E: A single table combining Patient, Doctor, Appointment, and Prescription into one. F: A separate table for each entity with foreign keys as specified, and an AppointmentDetails table linking Appointments to Prescriptions.
	ER Diagram and minimum tables ER Diagram	Data Modeling	Hard	2 mins	Solve
Look at the given ER diagram. What do you think is the least number of tables we would need to represent M, N, P, R1 and R2?
	Normalization Process Normalization Database Design Anomaly Elimination	Data Modeling	Medium	3 mins	Solve
Consider a healthcare database with a table named PatientRecords that stores patient visit information. The table has the following attributes: - VisitID - PatientID - PatientName - DoctorID - DoctorName - VisitDate - Diagnosis - Treatment - TreatmentCost In this table: - Each VisitID uniquely identifies a patient's visit and is associated with one PatientID. - PatientID is associated with exactly one PatientName. - Each DoctorID is associated with a unique DoctorName. - TreatmentCost is a fixed cost based on the Treatment. Evaluating the PatientRecords table, which of the following statements most accurately describes its normalization state and the required actions for higher normalization? A: The table is in 1NF. To achieve 2NF, remove partial dependencies by separating Patient information (PatientID, PatientName) and Doctor information (DoctorID, DoctorName) into different tables. B: The table is in 2NF. To achieve 3NF, remove transitive dependencies by creating separate tables for Patients (PatientID, PatientName), Doctors (DoctorID, DoctorName), and Visits (VisitID, PatientID, DoctorID, VisitDate, Diagnosis, Treatment, TreatmentCost). C: The table is in 3NF. To achieve BCNF, adjust for functional dependencies such as moving DoctorName to a separate Doctors table. D: The table is in 1NF. To achieve 3NF, create separate tables for Patients, Doctors, and Visits, and remove TreatmentCost as it is a derived attribute. E: The table is in 2NF. To achieve 4NF, address any multi-valued dependencies by separating Visit details and Treatment details. F: The table is in 3NF. To achieve 4NF, remove multi-valued dependencies related to VisitID.
	University Courses ER Diagrams Complex Relationships Integrity Constraints	Data Modeling	Medium	2 mins	Solve
Based on the ER diagram, which of the following statements is accurate and requires specific knowledge of the ER diagram's details? A: A Student can major in multiple Departments. B: An Instructor can belong to multiple Departments. C: A Course can be offered by multiple Departments. D: Enrollment records can link a Student to multiple Courses in a single semester. E: Each Course must be associated with an Enrollment record. F: A Department can offer courses without having any instructors.
	Dividends Financial Analysis Percentage and Average Calculations	Data Interpretation	Medium	3 mins	Solve
Consider the following line chart which shows the money invested by a company in production each year and the sales made by the company each year. If the pie chart shows the shareholding pattern of the company and the company gives 10% of the profit as dividends to its share holders then what is the average dividend received by retail investors from 2000 to 2004?
	Laptop Brands Proportions and Percentages Financial Reasoning	Data Interpretation	Medium	2 mins	Solve
Given below is the list of laptop brands and their details in which some data is missing. If the cost price of Dell is 3/5 of the cost price of Lenovo, then what will be the %profit of Dell?
	Median Trend Analysis Statistical Reasoning	Data Interpretation	Hard	3 mins	Solve
Consider the following line chart which shows the sales of five different companies from 2000 to 2009. Which of the following companies has the maximum percentage increase in the median from 2000 to 2004 and 2005 to 2009.
	Hiring Developer Skewed Data Graph	Data Analysis	Medium	3 mins	Solve
Two companies A and B hired developers from the year 2001 to 2005. The given bar graph shows the hiring details. Now select the statements that are true based on the given details. A: The data given for Company A is skewed to the left. B: The data given for Company B is skewed to the right. C: The data given for Company A is skewed to the right. D: For Company B, mean and mode are equal. E: For Company B, mean is equal to median but less than mode. F: For Company A, median is less than mode but greater than mean.
	Negative correlation	Data Analysis	Medium	2 mins	Solve
Saffi, one of the popular schools in San Francisco did a school wide study of the students in middle school. The study found that there is a negative correlation between the time spent on Facebook per day by students and their academic achievement. How can we understand the results of this study? A: An increase in time spent on Facebook per day causes a drop in the academic achievement of students at the middle school level. B: There is an association between an increase in time spent on Facebook per day and the drop in the academic achievement of students at Saffi. C: An increase in the time spent on Facebook per day causes a drop in the academic achievement of students at Saffi. D: There is an association between an increase in time spent on Facebook per day and the drop in the academic achievement of students at the middle school level.

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

Preview this test

Se prøvescorekort

Reason #5

Designed for elimination, not selection

The most important thing while implementing the pre-employment Data Wrangling 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 Data Wrangling 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

Se prøvescorekort

Preview this test

Se prøvescorekort

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

Preview this test

Se prøvescorekort

About the Data Wrangling Online Test

Why you should use Pre-employment Data Wrangling Test?

The Data Wrangling 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:

Dataekstraktion ved hjælp af forskellige metoder
Data parsing med præcision og nøjagtighed
Dataintegration for problemfri dataflow
Kvalitetsvurdering for at sikre dataintegritet
Datavalidering for at identificere og korrigere fejl
Datamodellering til effektiv repræsentation
Datatolkning for at udlede indsigt
Dataanalyse ved hjælp af statistiske teknikker
Håndtering af undtagelser og fejl i datamanipulation
Effektiv styring af store datasæt

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 Data Wrangling Test?

Data-parsing
Data-parsing involverer opdeling af komplekse datastrukturer i mindre , meningsfulde komponenter. Det er vigtigt at måle denne færdighed til at evaluere kandidatens evne til at udtrække specifikke oplysninger og manipulere data til videre behandling.
dataintegration
Dataintegration henviser til at kombinere data fra forskellige kilder til en Unified og konsistent format til analyse. Testning af denne færdighed hjælper med at bestemme kandidatens dygtighed til at fusionere datasæt og forberede dem til nøjagtig dataanalyse og rapportering.
kvalitetsvurdering
Kvalitetsvurdering involverer evaluering af pålideligheden, nøjagtigheden og fuldstændigheden af data . Denne færdighed måles i testen for at vurdere kandidatens evne til at identificere og rette op på uoverensstemmelser, fejl eller duplikationer i det indsamlede datasæt.
datavalidering
Datavalidering indebærer verifikation af integriteten og nøjagtighed af data ved at udføre valideringskontrol baseret på definerede regler eller begrænsninger. Måling af denne færdighed hjælper med at evaluere kandidatens dygtighed til at identificere og løse problemer med datakvalitet for at sikre datakonsistens og pålidelighed at understøtte effektiv datastyring og analyse. Denne færdighed måles i testen for at vurdere kandidatens evne til at skabe strukturerede datamodeller, der muliggør effektiv dataorganisation, opbevaring og hentning.
datatolkning
Datatolkning henviser til forståelsen og analyse af data for at udlede meningsfuld indsigt og tage informerede beslutninger. Testning af denne færdighed hjælper med at evaluere kandidatens evne til at fortolke datavisualisering, statistisk analyse og andre teknikker til at udtrække værdifuld information.
dataanalyse
Dataanalyse involverer anvendelse af statistiske og analytiske metoder til undersøgelse , transformere og evaluere data for at drage konklusioner og tage datadrevne beslutninger. Test af denne færdighed hjælper med at vurdere kandidatens dygtighed i at bruge forskellige teknikker og værktøjer til at afdække mønstre, tendenser, korrelationer og outliers i dataene.
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 Data Wrangling Test to be based on.
Dataekstraktion
Webskrabning
API -integration
Regex
Datarensning
Datatransformation
Data fusionering
Data deduplikation
Dataprofilering
Data validering
Data -revision
Modellering af enhedsforhold
Dimensionel modellering
Datatolkning
Datavisualisering
Beskrivende statistik
Hypotese testning
Regressions analyse
Tidsserieanalyse
Korrelationsanalyse
Prøveudtagning af data
Dataudforskning
Dataaggregering
Datafiltrering
Datatransformation
Datavisualiseringsteknikker
Dataklassificering
Dataklynge
Datasammendrag
Geospatial analyse

Preview this test

Se prøvescorekort

What roles can I use the Data Wrangling Test for?

Dataanalytiker
Dataforsker
Dataingeniør
Dataarkitekt
Business analytiker
Database Administrator
Datakvalitetsanalytiker
Specialisering af datavisualisering
Machine Learning Engineer
Kvantitativ analytiker

How is the Data Wrangling 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

Identificering af mønstre og tendenser i data
Anvendelse af datakranglingsteknikker til rengøring af data
Optimering af datalagring og hentning
Implementering af datatransformationsoperationer
Oprettelse af datarørledninger til automatisering
Udførelse af funktionsteknik til forudsigelig modellering
Implementering af avanceret datavisualisering
Gennemførelse af hypotesetest for datavalidering
Anvendelse af maskinlæringsalgoritmer til forudsigelig analyse
Design af datadrevne strategier for forretningsbeslutninger

Preview this test

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

Data Wrangling 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.