Why you should use Machine Learning Assessment Test?

The Vurdering af maskinlæring 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: Implementering af lineære regressionsmodeller til forudsigelig analyse Anvendelse af gradientafstamningsalgoritme til modeloptimering Identificering og afbødning af overfitting og underfitting af problemer i maskinlæringsmodeller Brug af supportvektormaskiner til klassificeringsopgaver At forstå begreberne bias og varians i maskinlæringsmodeller Udførelse af krydsvalidering for at vurdere ydelsen af overvågede læringsmodeller Anvendelse af forskellige teknikker i uovervåget læring såsom klynger Implementering af dimensionalitetsreduktionsmetoder til forbedring af modeleffektiviteten Evaluering af maskinindlæringsmodeller ved hjælp af passende evalueringsmålinger Brug af funktionsteknikker til at forbedre modelens ydeevne

Machine Learning Assessment Test

Q: What topics are covered in the Machine Learning Assessment Test?

Lineær regression Gradient afstamning Overfitting og underfitting Understøtt vektormaskiner Bias og varians Krydsvalidering Overvåget læring Uovervåget læring Klynger Dimensionalitetsreduktion Modelevaluering Funktionsteknik

About the test:

Evalueringstest for forudgående ansættelse vurderer testen for maskinlæring evaluerer en kandidats forståelse af grundlæggende til maskinlæring som funktionsteknik, regression, varians, betinget sandsynlighed, klynger, beslutningstræer, nærmeste naboer, naive bayes, bias og overfitting. Testen vurderer dem også på deres evne til at indsamle og forberede datasættet, træne en model, evaluere modellen og iterativt forbedre modellens ydelse.

Covered skills:

Lineær regression
Overfitting og underfitting
Bias og varians
Overvåget læring
Klynger
Modelevaluering

Gradient afstamning
Understøtt vektormaskiner
Krydsvalidering
Uovervåget læring
Dimensionalitetsreduktion
Funktionsteknik

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9 reasons why

Adaface Machine Learning Test is the most accurate way to shortlist Machine Learning Developers

Reason #1

Tests for on-the-job skills

The Machine Learning Assessment 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:

I stand til at implementere og forstå lineære regressionsalgoritmer
Dygtige til gradientafstamningsoptimeringsalgoritmer
Kendskab til koncepterne overfitting og underfitting i maskinlæringsmodeller
I stand til at anvende supportvektormaskiner til klassificeringsopgaver
I stand til at genkende og styre bias og varians i maskinlæringsmodeller
Dygtige i krydsvalideringsteknikker til modelevaluering
Erfarne i overvågede læringsalgoritmer
Kyndig i ikke -overvåget læringsalgoritmer
Kompetent til at udføre klyngeopgaver
I stand til at anvende dimensionalitetsreduktionsteknikker
Dygtige til evaluering af maskinlæringsmodeller
Dygtige til at udføre funktionsteknik til forbedring af modelydelsen

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 Vurdering af maskinlæring vil være ikke-gåbart.

🧐 Question
Easy Gradient descent optimization Gradient Descent	Try practice test
You are working on a regression problem using a simple neural network. You want to optimize the model's weights using gradient descent with different learning rate schedules. Consider the following pseudo code for training the neural network: Which of the following learning rate schedules would most likely result in the fastest convergence without overshooting the optimal weights? A: Constant learning rate of 0.01 B: Exponential decay with initial learning rate of 0.1 and decay rate of 0.99 C: Exponential decay with initial learning rate of 0.01 and decay rate of 0.99 D: Step decay with initial learning rate of 0.1 and decay rate of 0.5 every 100 epochs E: Step decay with initial learning rate of 0.01 and decay rate of 0.5 every 100 epochs F: Constant learning rate of 0.1
Medium Less complex decision tree model Model Complexity Overfitting	Try practice test
You are given a dataset to solve a classification problem using a decision tree algorithm. You are concerned about overfitting and decide to implement pruning to control the model's complexity. Consider the following pseudo code for creating the decision tree model: Which of the following combinations of parameters would result in a less complex decision tree model, reducing the risk of overfitting? A: max_depth=5, min_samples_split=2, min_samples_leaf=1 B: max_depth=None, min_samples_split=5, min_samples_leaf=5 C: max_depth=3, min_samples_split=2, min_samples_leaf=1 D: max_depth=None, min_samples_split=2, min_samples_leaf=1 E: max_depth=3, min_samples_split=10, min_samples_leaf=10 F; max_depth=5, min_samples_split=5, min_samples_leaf=5
Easy n-gram generator	Try practice test
Our newest machine learning developer want to write a function to calculate the n-gram of any text. An N-gram means a sequence of N words. So for example, "black cats" is a 2-gram, "saw black cats" is a 3-gram etc. The 2-gram of the sentence "the big bad wolf fell down" would be [["the", "big"], ["big", "bad"], ["bad", "wolf"], ["wolf", "fell"], ["fell", "down"]]. Can you help them select the correct function for the same?
Easy Recommendation System Selection Recommender Systems Collaborative Filtering Content-Based Filtering	Try practice test
You are tasked with building a recommendation system for a newly launched e-commerce website. Given that the website is new, there is not much user interaction data available. Also, the items in the catalog have rich descriptions. Based on these requirements, which type of recommendation system approach would be the most suitable for this task?
Easy Sensitivity and Specificity Confusion Matrix Model Evaluation	Try practice test
You have trained a supervised learning model to classify customer reviews as either "positive" or "negative" based on a dataset with 10,000 samples and 35 features, including the review text, reviewer's name, and rating. The dataset is split into a 7,000-sample training set and a 3,000-sample test set. After training the model, you evaluate its performance using a confusion matrix on the test set, which shows the following results: Based on the confusion matrix, what are the sensitivity and specificity of the model?

	🧐 Question	🔧 Skill
	Easy Gradient descent optimization Gradient Descent	2 mins Machine Learning	Try practice test
You are working on a regression problem using a simple neural network. You want to optimize the model's weights using gradient descent with different learning rate schedules. Consider the following pseudo code for training the neural network: Which of the following learning rate schedules would most likely result in the fastest convergence without overshooting the optimal weights? A: Constant learning rate of 0.01 B: Exponential decay with initial learning rate of 0.1 and decay rate of 0.99 C: Exponential decay with initial learning rate of 0.01 and decay rate of 0.99 D: Step decay with initial learning rate of 0.1 and decay rate of 0.5 every 100 epochs E: Step decay with initial learning rate of 0.01 and decay rate of 0.5 every 100 epochs F: Constant learning rate of 0.1
	Medium Less complex decision tree model Model Complexity Overfitting	2 mins Machine Learning	Try practice test
You are given a dataset to solve a classification problem using a decision tree algorithm. You are concerned about overfitting and decide to implement pruning to control the model's complexity. Consider the following pseudo code for creating the decision tree model: Which of the following combinations of parameters would result in a less complex decision tree model, reducing the risk of overfitting? A: max_depth=5, min_samples_split=2, min_samples_leaf=1 B: max_depth=None, min_samples_split=5, min_samples_leaf=5 C: max_depth=3, min_samples_split=2, min_samples_leaf=1 D: max_depth=None, min_samples_split=2, min_samples_leaf=1 E: max_depth=3, min_samples_split=10, min_samples_leaf=10 F; max_depth=5, min_samples_split=5, min_samples_leaf=5
	Easy n-gram generator	2 mins Machine Learning	Try practice test
Our newest machine learning developer want to write a function to calculate the n-gram of any text. An N-gram means a sequence of N words. So for example, "black cats" is a 2-gram, "saw black cats" is a 3-gram etc. The 2-gram of the sentence "the big bad wolf fell down" would be [["the", "big"], ["big", "bad"], ["bad", "wolf"], ["wolf", "fell"], ["fell", "down"]]. Can you help them select the correct function for the same?
	Easy Recommendation System Selection Recommender Systems Collaborative Filtering Content-Based Filtering	2 mins Machine Learning	Try practice test
You are tasked with building a recommendation system for a newly launched e-commerce website. Given that the website is new, there is not much user interaction data available. Also, the items in the catalog have rich descriptions. Based on these requirements, which type of recommendation system approach would be the most suitable for this task?
	Easy Sensitivity and Specificity Confusion Matrix Model Evaluation	2 mins Machine Learning	Try practice test
You have trained a supervised learning model to classify customer reviews as either "positive" or "negative" based on a dataset with 10,000 samples and 35 features, including the review text, reviewer's name, and rating. The dataset is split into a 7,000-sample training set and a 3,000-sample test set. After training the model, you evaluate its performance using a confusion matrix on the test set, which shows the following results: Based on the confusion matrix, what are the sensitivity and specificity of the model?

	🧐 Question	🔧 Skill	💪 Difficulty	⌛ Time
	Gradient descent optimization Gradient Descent	Machine Learning	Easy	2 mins	Try practice test
You are working on a regression problem using a simple neural network. You want to optimize the model's weights using gradient descent with different learning rate schedules. Consider the following pseudo code for training the neural network: Which of the following learning rate schedules would most likely result in the fastest convergence without overshooting the optimal weights? A: Constant learning rate of 0.01 B: Exponential decay with initial learning rate of 0.1 and decay rate of 0.99 C: Exponential decay with initial learning rate of 0.01 and decay rate of 0.99 D: Step decay with initial learning rate of 0.1 and decay rate of 0.5 every 100 epochs E: Step decay with initial learning rate of 0.01 and decay rate of 0.5 every 100 epochs F: Constant learning rate of 0.1
	Less complex decision tree model Model Complexity Overfitting	Machine Learning	Medium	2 mins	Try practice test
You are given a dataset to solve a classification problem using a decision tree algorithm. You are concerned about overfitting and decide to implement pruning to control the model's complexity. Consider the following pseudo code for creating the decision tree model: Which of the following combinations of parameters would result in a less complex decision tree model, reducing the risk of overfitting? A: max_depth=5, min_samples_split=2, min_samples_leaf=1 B: max_depth=None, min_samples_split=5, min_samples_leaf=5 C: max_depth=3, min_samples_split=2, min_samples_leaf=1 D: max_depth=None, min_samples_split=2, min_samples_leaf=1 E: max_depth=3, min_samples_split=10, min_samples_leaf=10 F; max_depth=5, min_samples_split=5, min_samples_leaf=5
	n-gram generator	Machine Learning	Easy	2 mins	Try practice test
Our newest machine learning developer want to write a function to calculate the n-gram of any text. An N-gram means a sequence of N words. So for example, "black cats" is a 2-gram, "saw black cats" is a 3-gram etc. The 2-gram of the sentence "the big bad wolf fell down" would be [["the", "big"], ["big", "bad"], ["bad", "wolf"], ["wolf", "fell"], ["fell", "down"]]. Can you help them select the correct function for the same?
	Recommendation System Selection Recommender Systems Collaborative Filtering Content-Based Filtering	Machine Learning	Easy	2 mins	Try practice test
You are tasked with building a recommendation system for a newly launched e-commerce website. Given that the website is new, there is not much user interaction data available. Also, the items in the catalog have rich descriptions. Based on these requirements, which type of recommendation system approach would be the most suitable for this task?
	Sensitivity and Specificity Confusion Matrix Model Evaluation	Machine Learning	Easy	2 mins	Try practice test
You have trained a supervised learning model to classify customer reviews as either "positive" or "negative" based on a dataset with 10,000 samples and 35 features, including the review text, reviewer's name, and rating. The dataset is split into a 7,000-sample training set and a 3,000-sample test set. After training the model, you evaluate its performance using a confusion matrix on the test set, which shows the following results: Based on the confusion matrix, what are the sensitivity and specificity of the model?

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 Vurdering af maskinlæring 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 Vurdering af maskinlæring from your dashboard by entering their email address.

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

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

Reason #7

Detailed scorecards & benchmarks

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Reason #8

High completion rate

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

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

Reason #9

Advanced Proctoring

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About the Machine Learning Online Test

Why you should use Pre-employment Machine Learning Assessment Test?

The Vurdering af maskinlæring 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:

Implementering af lineære regressionsmodeller til forudsigelig analyse
Anvendelse af gradientafstamningsalgoritme til modeloptimering
Identificering og afbødning af overfitting og underfitting af problemer i maskinlæringsmodeller
Brug af supportvektormaskiner til klassificeringsopgaver
At forstå begreberne bias og varians i maskinlæringsmodeller
Udførelse af krydsvalidering for at vurdere ydelsen af overvågede læringsmodeller
Anvendelse af forskellige teknikker i uovervåget læring såsom klynger
Implementering af dimensionalitetsreduktionsmetoder til forbedring af modeleffektiviteten
Evaluering af maskinindlæringsmodeller ved hjælp af passende evalueringsmålinger
Brug af funktionsteknikker til at forbedre modelens ydeevne

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 Machine Learning Assessment Test?

Overfitting og underfitting
Overfitting opstår, når en maskinlæringsmodel også passer til træningsdata Tæt, hvilket fører til dårlig generalisering og ydeevne på usete data. Underfitting sker derimod, når modellen er for enkel og ikke fanger de underliggende mønstre i dataene. Evaluering af en kandidats forståelse af overfitting og underfitting hjælper med at måle deres viden i modelkompleksitet og deres evne til at finde den rigtige balance for optimal ydelse.
understøtter vektormaskiner
Support Vector Machines (SVM) er overvågede læringsalgoritmer, der bruges til klassificerings- og regressionsopgaver. De finder en optimal hyperplan, der adskiller forskellige klasser eller forudsiger kontinuerlige værdier. Måling af denne færdighed hjælper rekrutterere med at evaluere kandidatens kompetence til at anvende SVM'er og deres evne til at håndtere både lineær og ikke-lineær klassificering eller regressionsproblemer.
bias og varians
bias refererer til Fejl indført af en modells alt for forenklede antagelser, mens variansen måler modellens følsomhed over for udsving i træningsdataene. Disse to koncepter hjælper med at forstå udvekslingen mellem underfitting og overfitting. Evaluering af en kandidats viden om bias og varians gør det muligt for rekrutterere at vurdere deres forståelse af modelpræstation og evnen til at finjustere modeller for bedre resultater.
krydsvalidering
krydsvalidering er En teknik, der bruges til at vurdere ydelsen og generaliseringsfunktionerne i maskinlæringsmodeller. Det involverer opdeling af dataene i flere undergrupper til træning og test, hvilket muliggør en mere robust evaluering af en model's ydelse. Evaluering af en kandidats viden om krydsvalidering hjælper med at bestemme deres ekspertise inden for modelevaluering og deres evne til at undgå overoptimistiske estimater En model lærer af mærkede data for at foretage forudsigelser eller klassifikationer. Det involverer at have en klar målvariabel, som modellen sigter mod at forudsige. Evaluering af denne færdighed hjælper med at måle en kandidats forståelse af overvågede læringsalgoritmer og deres evne til at anvende dem på forskellige forudsigelsesopgaver.
Uovervåget læring
Uovervåget læring er en maskinlæringsopgave, hvor en model lærer Fra umærkede data til at finde mønstre eller strukturer uden specifikke målvariabler. Denne færdighed måler en kandidats fortrolighed med uovervågede læringsalgoritmer, såsom klynger og dimensionalitetsreduktion, og deres evne til at udtrække meningsfuld indsigt fra ustrukturerede data.
klynger
klynger er en uovervåget læringsteknik Disse grupperer lignende datapunkter sammen baseret på deres egenskaber eller ligheder. Det hjælper med at identificere naturlige strukturer eller kategorier inden for data. Evaluering af en kandidats viden om klynge -algoritmer betyder deres færdigheder i at udforske mønstre inden for data og deres evne til at segmentere datasæt i meningsfulde klynger til yderligere analyse.
dimensionalitetsreduktion
Dimensionalitetsreduktion er processen med Reduktion af antallet af inputvariabler/funktioner i maskinlæringsmodeller. Det hjælper med at forenkle komplekse datasæt ved at fjerne overflødige eller irrelevante funktioner, samtidig med at de opretholder vigtige oplysninger. Evaluering af denne færdighed giver rekrutterere mulighed for at evaluere en kandidats forståelse af funktionsvalgsteknikker og deres evne til at forbedre modelydelsen og fortolkbarheden.
modelevaluering
Modelevaluering er processen med at vurdere ydelsen og Kvalitet af maskinlæringsmodeller. Det involverer at bruge forskellige målinger og teknikker til at måle, hvor godt en model generaliseres til usete data. Evaluering af denne færdighed hjælper rekrutterere med at bestemme en kandidats færdighed i evaluering og sammenligning af forskellige modeller og deres evne til at vælge den mest passende til en given opgave.
funktionsteknik
Feature Engineering er processen er processen At oprette nye funktioner eller omdanne eksisterende for at forbedre ydelsen af maskinlæringsmodeller. Det involverer at vælge, oprette eller ændre variabler for bedre at repræsentere de underliggende mønstre i dataene. Måling af denne færdighed gør det muligt for rekrutterere at vurdere en kandidats ekspertise i at forbedre modellernes forudsigelsesevne gennem indsigtsfulde funktionsteknikker.
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 Vurdering af maskinlæring to be based on.
Lineær regression
Almindelige mindst kvadrater
Gradient afstamning
Stokastisk gradientafstamning
Batch gradient afstamning
Ridge Regression
Lasso -regression
Polynomisk regression
Regularisering
Overfitting
Underfitting
Understøtt vektormaskiner
Kerne tricks
Hyperplan
Blød margin
Hård margin
Partiskhed
Varians
Krydsvalidering
K-fold krydsvalidering
Forlad en-ud krydsvalidering
Holdout -metode
Overvåget læring
Klassifikation
Regression
Beslutningstræer
Tilfældige skove
Naive Bayes
K-nærmeste naboer
Neurale netværk
Uovervåget læring
Klynger
K-middel
Hierarkisk
DBSCAN
Dimensionalitetsreduktion
PCA (hovedkomponentanalyse)
LDA (lineær diskriminerende analyse)
T-SNE (T-distribueret stokastisk nabo.
Modelevaluering
Nøjagtighed
Præcision
Minde om
F1 score
ROC -kurve
AUC (område under kurven)
Forvirringsmatrix
Funktionsteknik
Datatransformation
Funktionsskalering
Dummy variabler
Variable interaktioner
Håndtering af manglende data
Outlier -detektion

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What roles can I use the Machine Learning Assessment Test for?

Machine Learning Developer
Machine Learning Engineer
Dataforsker
Dataanalytiker
Kunstig efterretningsingeniør
Dataingeniør
Business analytiker
Forsker
Statistisk analytiker
Data mining specialist

How is the Machine Learning Assessment 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

Implementering af beslutningstræer og tilfældige skove til klassificeringsopgaver
Anvendelse af ensemble -metoder såsom posning og øget for at forbedre modelydelsen
At forstå koncepter og anvendelser af neurale netværk
Implementering af dybe læringsmodeller til komplekse opgaver
Brug af naturlige sprogbehandlingsteknikker til tekstklassificering og sentimentanalyse
Anvendelse af anbefalingssystemer til personaliserede anbefalinger
Forståelse af koncepter og anvendelser af forstærkningslæring
Implementering af tidsserieanalyse til forudsigelse af fremtidige tendenser
Brug af anomali -detektionsteknikker til at identificere usædvanlige mønstre i data
Anvendelse af overførselslæring for at udnytte viden fra foruddannede modeller

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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 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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Machine Learning Hiring Test Ofte stillede spørgsmål

Hvilken type spørgsmål inkluderer maskinlæringstesten?

Denne forudgående ansættelsesmaskinindlæringstest består af scenariebaserede spørgsmål, der kræver, at kandidater demonstrerer deres evne til at:

Forbered data til maskinlæringsalgoritmer
Brug ML -algoritmer som logistisk regression, supportvektormaskiner, beslutningstræer og tilfældige skove til klassificering
Byg klynge -algoritmer
foreslå den mest passende algoritme til en bestemt brugssag
Estimer ydelse af læringsalgoritmer

Kan denne test eller vurdering bruges til senior maskinlæringsingeniørroller?

For senior maskinlæringsingeniører kan du anmode om en brugerdefineret test. Inden for 48 timer vil vores emneeksperter tilpasse vurderingen i overensstemmelse med din jobbeskrivelse og anciennitetsniveau. En typisk test for seniorroller, ud over grundlæggende forhold, vil testen fokusere på at teste en kandidats evne til at:

Struktur ML -projekter
Identificer mangler ved forskellige maskinlæringsalgoritmer
Design en data om rengøring og dataremikling
Vælg ordentlige evalueringsmetrik for at forbedre modelydelsen
Vurder virkningen af hardwareydelse på maskinlæringsalgoritmerne

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.