Why you should use Python Pandas Online Test?

The Python Pandas 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: Læsning og skrivning af data ved hjælp af Python Datamanipulation ved hjælp af pandaer Dataanalyse ved hjælp af Python Datarensning og forarbejdning Datavisualisering med pandaer Arbejder med tidsseriedata ved hjælp af Pandas Gruppering og aggregering af data med pandaer Fusion og sammenføjning af Dataframes med pandaer Håndtering af manglende data med pandaer Anvendelse af statistiske funktioner ved hjælp af pandaer

Python Pandas Online Test

Q: What topics are covered in the Python Pandas Online Test?

Læsning og skrivedata Datamanipulation Dataanalyse Datarensning og forarbejdning Datavisualisering Arbejder med tidsseriedata Gruppering og aggregering af data Fusion og sammenføjning af Dataframes Håndtering af manglende data Anvendelse af statistiske funktioner Omformning af data

About the test:

Python Pandas online -test evaluerer en kandidats evne til at arbejde med data ved hjælp af Pandas -biblioteket i Python. Den vurderer viden om læsning og skrivning af data, datamanipulation, analyse, rengøring, datavisualisering, tidsseriedatahåndtering, gruppering og aggregering, fusion og sammenføjning af dataframes, manglende datahåndtering, anvendelse af statistiske funktioner og omformning af data.

Covered skills:

Læsning og skrivedata
Dataanalyse
Datavisualisering
Gruppering og aggregering af data
Håndtering af manglende data
Omformning af data

Datamanipulation
Datarensning og forarbejdning
Arbejder med tidsseriedata
Fusion og sammenføjning af Dataframes
Anvendelse af statistiske funktioner

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9 reasons why

Adaface Python Pandas Test is the most accurate way to shortlist Python -udviklers

Reason #1

Tests for on-the-job skills

The Python Pandas 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:

Læsning og skrivning af data effektivt ved hjælp af Python Pandas
Udførelse af datamanipulationsoperationer ved hjælp af Python Pandas
Analyse af data ved hjælp af Python Pandas -biblioteket
Rengøring og forarbejdning af data ved hjælp af Python Pandas
Visualisering af data ved hjælp af Python Pandas
Arbejder med tidsseriedata i Python Pandas
Gruppering og aggregering af data ved hjælp af Python Pandas
Fusion og sammenføjning af Dataframes i Python Pandas
Håndtering af manglende data ved hjælp af Python Pandas
Anvendelse af statistiske funktioner på data ved hjælp af Python Pandas
Omformning af data ved hjælp af Python Pandas

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 Python Pandas Test vil være ikke-gåbart.

🧐 Question
Medium ZeroDivisionError and IndexError Exceptions	Try practice test
What will the following Python code output?
Medium Session File Handling Dictionary	Try practice test
The function high_sess should compute the highest number of events per session of each user in the database by reading a comma-separated value input file of session data. The result should be returned from the function as a dictionary. The first column of each line in the input file is expected to contain the user’s name represented as a string. The second column is expected to contain an integer representing the events in a session. Here is an example input file: Tony,10 Stark,12 Black,25 Your program should ignore a non-conforming line like this one. Stark,3 Widow,6 Widow,14 The resulting return value for this file should be the following dictionary: { 'Stark':12, 'Black':25, 'Tony':10, 'Widow':14 } What should replace the CODE TO FILL line to complete the function?
Medium Max Code Arrays	Try practice test
Below are code lines to create a Python function. Ignoring indentation, what lines should be used and in what order for the following function to be complete:
Medium Recursive Function Recursion Dictionary Lists	Try practice test
Consider the following Python code: In the above code, recursive_search is a function that takes a dictionary (data) and a target key (target) as arguments. It searches for the target key within the dictionary, which could potentially have nested dictionaries and lists as values, and returns the value associated with the target key. If the target key is not found, it returns None. nested_dict is a dictionary that contains multiple levels of nested dictionaries and lists. The recursive_search function is then called with nested_dict as the data and 'target_key' as the target. What will the output be after executing the above code?
Medium Stacking problem Stack Linkedlist	Try practice test
What does the below function ‘fun’ does? A: Sum of digits of the number passed to fun. B: Number of digits of the number passed to fun. C: 0 if the number passed to fun is divisible by 10. 1 otherwise. D: Sum of all digits number passed to fun except for the last digit.
Medium Data Aggregation and Transformation Data Aggregation Data Transformation	Try practice test
You are working with a dataset, `df`, that contains columns 'A', 'B', and 'C'. You need to perform the following tasks: 1. Group the DataFrame `df` by column 'A'. 2. Compute the sum of column 'B' for each group. 3. Append this sum as a new column 'D' to the original DataFrame `df`. You wrote the following code to perform these tasks: However, you notice that the new column 'D' contains many missing values. What is the cause of this issue? A: The groupby method did not work as expected. B: The sum method did not work as expected. C: The new column 'D' should be appended to grouped instead of df. D: The grouped object should be mapped to df['A'] before assigning to a new column in df E: The groupby method should be called on df['A'] instead of df.
Easy Handling Missing Data Data Cleaning Missing Data	Try practice test
You are working with a dataset, `df`, that contains several columns with missing values. You want to replace all missing values in the dataset with the mean of the non-missing values of their respective columns. You wrote the following code to perform this task: However, you notice that some missing values are still not replaced. What is the cause of this issue? A: The fillna method does not work with the mean method. B: The mean method does not work with missing values. C: The fillna method should be called on df.mean() instead of df. D: The fillna method does not work inplace by default. You should use df.fillna(df.mean(), inplace=True). E: The mean method should be called on df.fillna() instead of df.

	🧐 Question	🔧 Skill
	Medium ZeroDivisionError and IndexError Exceptions	2 mins Python	Try practice test
What will the following Python code output?
	Medium Session File Handling Dictionary	2 mins Python	Try practice test
The function high_sess should compute the highest number of events per session of each user in the database by reading a comma-separated value input file of session data. The result should be returned from the function as a dictionary. The first column of each line in the input file is expected to contain the user’s name represented as a string. The second column is expected to contain an integer representing the events in a session. Here is an example input file: Tony,10 Stark,12 Black,25 Your program should ignore a non-conforming line like this one. Stark,3 Widow,6 Widow,14 The resulting return value for this file should be the following dictionary: { 'Stark':12, 'Black':25, 'Tony':10, 'Widow':14 } What should replace the CODE TO FILL line to complete the function?
	Medium Max Code Arrays	2 mins Python	Try practice test
Below are code lines to create a Python function. Ignoring indentation, what lines should be used and in what order for the following function to be complete:
	Medium Recursive Function Recursion Dictionary Lists	3 mins Python	Try practice test
Consider the following Python code: In the above code, recursive_search is a function that takes a dictionary (data) and a target key (target) as arguments. It searches for the target key within the dictionary, which could potentially have nested dictionaries and lists as values, and returns the value associated with the target key. If the target key is not found, it returns None. nested_dict is a dictionary that contains multiple levels of nested dictionaries and lists. The recursive_search function is then called with nested_dict as the data and 'target_key' as the target. What will the output be after executing the above code?
	Medium Stacking problem Stack Linkedlist	4 mins Python	Try practice test
What does the below function ‘fun’ does? A: Sum of digits of the number passed to fun. B: Number of digits of the number passed to fun. C: 0 if the number passed to fun is divisible by 10. 1 otherwise. D: Sum of all digits number passed to fun except for the last digit.
	Medium Data Aggregation and Transformation Data Aggregation Data Transformation	2 mins Pandas	Try practice test
You are working with a dataset, `df`, that contains columns 'A', 'B', and 'C'. You need to perform the following tasks: 1. Group the DataFrame `df` by column 'A'. 2. Compute the sum of column 'B' for each group. 3. Append this sum as a new column 'D' to the original DataFrame `df`. You wrote the following code to perform these tasks: However, you notice that the new column 'D' contains many missing values. What is the cause of this issue? A: The groupby method did not work as expected. B: The sum method did not work as expected. C: The new column 'D' should be appended to grouped instead of df. D: The grouped object should be mapped to df['A'] before assigning to a new column in df E: The groupby method should be called on df['A'] instead of df.
	Easy Handling Missing Data Data Cleaning Missing Data	2 mins Pandas	Try practice test
You are working with a dataset, `df`, that contains several columns with missing values. You want to replace all missing values in the dataset with the mean of the non-missing values of their respective columns. You wrote the following code to perform this task: However, you notice that some missing values are still not replaced. What is the cause of this issue? A: The fillna method does not work with the mean method. B: The mean method does not work with missing values. C: The fillna method should be called on df.mean() instead of df. D: The fillna method does not work inplace by default. You should use df.fillna(df.mean(), inplace=True). E: The mean method should be called on df.fillna() instead of df.

	🧐 Question	🔧 Skill	💪 Difficulty	⌛ Time
	ZeroDivisionError and IndexError Exceptions	Python	Medium	2 mins	Try practice test
What will the following Python code output?
	Session File Handling Dictionary	Python	Medium	2 mins	Try practice test
The function high_sess should compute the highest number of events per session of each user in the database by reading a comma-separated value input file of session data. The result should be returned from the function as a dictionary. The first column of each line in the input file is expected to contain the user’s name represented as a string. The second column is expected to contain an integer representing the events in a session. Here is an example input file: Tony,10 Stark,12 Black,25 Your program should ignore a non-conforming line like this one. Stark,3 Widow,6 Widow,14 The resulting return value for this file should be the following dictionary: { 'Stark':12, 'Black':25, 'Tony':10, 'Widow':14 } What should replace the CODE TO FILL line to complete the function?
	Max Code Arrays	Python	Medium	2 mins	Try practice test
Below are code lines to create a Python function. Ignoring indentation, what lines should be used and in what order for the following function to be complete:
	Recursive Function Recursion Dictionary Lists	Python	Medium	3 mins	Try practice test
Consider the following Python code: In the above code, recursive_search is a function that takes a dictionary (data) and a target key (target) as arguments. It searches for the target key within the dictionary, which could potentially have nested dictionaries and lists as values, and returns the value associated with the target key. If the target key is not found, it returns None. nested_dict is a dictionary that contains multiple levels of nested dictionaries and lists. The recursive_search function is then called with nested_dict as the data and 'target_key' as the target. What will the output be after executing the above code?
	Stacking problem Stack Linkedlist	Python	Medium	4 mins	Try practice test
What does the below function ‘fun’ does? A: Sum of digits of the number passed to fun. B: Number of digits of the number passed to fun. C: 0 if the number passed to fun is divisible by 10. 1 otherwise. D: Sum of all digits number passed to fun except for the last digit.
	Data Aggregation and Transformation Data Aggregation Data Transformation	Pandas	Medium	2 mins	Try practice test
You are working with a dataset, `df`, that contains columns 'A', 'B', and 'C'. You need to perform the following tasks: 1. Group the DataFrame `df` by column 'A'. 2. Compute the sum of column 'B' for each group. 3. Append this sum as a new column 'D' to the original DataFrame `df`. You wrote the following code to perform these tasks: However, you notice that the new column 'D' contains many missing values. What is the cause of this issue? A: The groupby method did not work as expected. B: The sum method did not work as expected. C: The new column 'D' should be appended to grouped instead of df. D: The grouped object should be mapped to df['A'] before assigning to a new column in df E: The groupby method should be called on df['A'] instead of df.
	Handling Missing Data Data Cleaning Missing Data	Pandas	Easy	2 mins	Try practice test
You are working with a dataset, `df`, that contains several columns with missing values. You want to replace all missing values in the dataset with the mean of the non-missing values of their respective columns. You wrote the following code to perform this task: However, you notice that some missing values are still not replaced. What is the cause of this issue? A: The fillna method does not work with the mean method. B: The mean method does not work with missing values. C: The fillna method should be called on df.mean() instead of df. D: The fillna method does not work inplace by default. You should use df.fillna(df.mean(), inplace=True). E: The mean method should be called on df.fillna() instead of df.

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 Python Pandas 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 Python Pandas 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 Python Pandas Assessment Test

Why you should use Pre-employment Python Pandas Online Test?

The Python Pandas 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:

Læsning og skrivning af data ved hjælp af Python
Datamanipulation ved hjælp af pandaer
Dataanalyse ved hjælp af Python
Datarensning og forarbejdning
Datavisualisering med pandaer
Arbejder med tidsseriedata ved hjælp af Pandas
Gruppering og aggregering af data med pandaer
Fusion og sammenføjning af Dataframes med pandaer
Håndtering af manglende data med pandaer
Anvendelse af statistiske funktioner ved hjælp af pandaer

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 Python Pandas Online Test?

Data Manipulation </h4 > <p> Datamanipulation henviser til processen med at transformere og ændre data for at gøre dem egnede til analyse. Det inkluderer opgaver såsom filtreringsrækker baseret på visse betingelser, ændring af datatyper, skabelse af nye kolonner, manipulering af strenge og udførelse af matematiske operationer på data. Denne færdighed skal måles i denne test, fordi det er et vigtigt aspekt af dataanalyse, hvilket giver brugerne mulighed for at omdanne rå data til et struktureret og anvendeligt format til yderligere analyse. </p> <h4> dataanalyse </H4> <p> Dataanalyse involverer at udforske og give mening om data, identificere mønstre, korrelationer og tendenser og udtrække meningsfuld indsigt. Det inkluderer opgaver, såsom beregning af sammenfattende statistikker, beregning af frekvenser, udførelse af aggregeringer og anvendelse af statistiske funktioner. Det er vigtigt at måle denne færdighed i testen, da den vurderer kandidatens evne til at anvende forskellige dataanalyseteknikker ved hjælp af Python Pandas -biblioteket og derved bestemme deres færdigheder i analyse og fortolkning af data. </p> <h4> Datarensning og forarbejdning </h4 > <p> Datarensning og forarbejdning involverer at identificere og håndtere manglende eller forkerte data, fjerne duplikater, håndtere outliers, normalisere data og udføre andre datarensningsoperationer. Denne færdighed er vigtig for at sikre dataintegritet og nøjagtighed, inden du udfører yderligere analyse. Måling af denne færdighed i testen hjælper med at evaluere kandidatens evne til at rengøre og forbehandle data effektivt, hvilket er et kritisk trin i dataanalyseprocessen. </p> <h4> datavisualisering
Datavisualisering refererer til at repræsentere Data i et visuelt format, såsom diagrammer, grafer og kort, for at lette forståelse og kommunikation af information. Det inkluderer opgaver såsom at skabe grunde, tilpasse visualiseringer, tilføje etiketter, farver og sagn og visualisering af tendenser og forhold i data. Måling af denne færdighed i testen giver indsigt i kandidatens evne til visuelt at repræsentere data ved hjælp af Python Pandas -biblioteket, hvilket er vigtigt for effektiv datafortælling og præsentation.
Arbejder med tidsseriedata </h4> <P > Arbejde med tidsseriedata involverer håndtering og analyse af data, der bestilles og indekseres efter tid eller dato. Det inkluderer opgaver såsom tidsbaseret indeksering, resampling af data ved forskellige frekvenser, beregning af rullende statistik og arbejde med tidsrelaterede operationer. Måling af denne færdighed i testen vurderer kandidatens evne til at arbejde med tidsseriedata ved hjælp af Python Pandas -biblioteket, som er afgørende i domæner såsom finansiering, aktiemarkedsanalyse og prognoser. </p> <H4> Gruppering og aggregerende data < /H4> <p> Gruppering og aggregering af data involverer gruppering af data efter en eller flere kategoriske variabler og derefter anvendelse af samlede funktioner til beregning af resume af statistikker inden for hver gruppe. Det inkluderer opgaver såsom gruppering af data efter specifikke kolonner, udførelse af samlede beregninger såsom gennemsnit, sum, tælling og anvendelse af brugerdefinerede aggregeringsfunktioner. Måling af denne færdighed i testen evaluerer kandidatens færdigheder i gruppering og opsummering af data effektivt ved hjælp af Python Pandas -biblioteket, hvilket er vigtigt for dataanalyse og generering af indsigt. </p> <h4> fusionering og sammenføjning af Dataframes
Fusion og sammenføjning af dataframes involverer at kombinere flere dataframes baseret på fælles kolonner eller indekser og derved oprette et nyt dataframe, der indeholder alle oplysninger fra de fusionerede datasæt. Det inkluderer opgaver såsom indre og ydre sammenføjninger, fusionering af flere nøgler, sammenkædning af dataframes lodret eller vandret og håndtering af overlappende kolonneavne. Måling af denne færdighed i testen vurderer kandidatens evne til at fusionere og deltage i dataframes nøjagtigt og effektivt ved hjælp af Python Pandas -biblioteket, som er en vigtig færdighed til at integrere og harmonisere data fra forskellige kilder.
håndtering af manglende data <////// H4> <p> Håndtering af manglende data involverer at identificere, analysere og udfylde manglende værdier eller slette rækker/kolonner med manglende data. Det inkluderer opgaver såsom at detektere manglende værdier, beregne manglende værdier ved hjælp af strategier som middelværdi, median eller interpolering og fjerne rækker eller kolonner med overdreven manglende data. Måling af denne færdighed i testen hjælper med at evaluere kandidatens evne til at håndtere manglende data passende ved hjælp af Python Pandas -biblioteket, som er afgørende for at sikre datakvalitet og integritet under analyseprocessen. </p> <h4> Anvendelse af statistiske funktioner
Anvendelse af statistiske funktioner involverer udførelse af statistiske beregninger og analyser af data, såsom beregning af korrelationskoefficienter, udførelse af hypotesetest, måling af central tendens og variation og implementering af statistiske modeller. Det inkluderer opgaver såsom beregning af gennemsnit, median, tilstand, varians, standardafvigelse og anvendelse af inferentielle statistikmetoder. Måling af denne færdighed i testen vurderer kandidatens færdigheder i at anvende statistiske funktioner fra Python Pandas -biblioteket for at udlede meningsfulde indsigt og konklusioner fra de data, der analyseres.
omformning af data
omformning af data involverer involverer Transformering af datastrukturen, der passer til specifikke analysekrav eller ønskede formater. Det inkluderer opgaver såsom drejningsdata, smeltedata, stabling og ustacking af data og transformering af bredformatdata til langformat eller omvendt. Måling af denne færdighed i testen evaluerer kandidatens evne til at omforme, omstrukturere og organisere data effektivt ved hjælp af Python Pandas -biblioteket, hvilket er vigtigt for dataanalyse, modellering og rapportering.
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 Python Pandas Test to be based on.
Læsning af CSV -filer
Skrivning af CSV -filer
Læsning af Excel -filer
Skrivning af Excel -filer
Filtrering af data
Sortering af data
Deltag i data
Gruppering af data
Aggregerende data
Håndtering af duplikater
Håndtering af manglende værdier
Datavisualisering
Linjeplotter
Histogrammer
Spredning af plot
Boks plot
Tidsserieanalyse
Resampling -tidsserier
Håndtering af tidszoner
Omformning af data
Drivende data
Smeltedata
Statistisk analyse
Beskrivende statistik
Korrelationsanalyse
Hypotese testning
Lineær regression
Datarensningsteknikker
Data -imputation
Outlier -detektion
Datatransformation
Datalormalisering

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What roles can I use the Python Pandas Online Test for?

Python -udvikler
Python Data Engineer
Dataanalytiker
Dataforsker
Dataingeniør
Machine Learning Engineer
Database Administrator

How is the Python Pandas 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

Omformning af data ved hjælp af pandaer
Integrering af Python med andre teknologier
Optimering af databehandlingsrørledninger i Python
Debugging og fejlfinding af datarelaterede problemer
Effektiv behandling af store datasæt
Anvendelse af maskinlæringsalgoritmer til dataanalyse
Implementering af datatilgang og sikkerhedsforanstaltninger
Bygning af interaktive datarashboards
Automatisering af dataanalyse arbejdsgange
Samarbejde med tværfunktionelle teams til datadrevet beslutningstagning

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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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Python Pandas 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.