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About the test:

Computer Vision Test evaluerer en kandidats viden og forståelse af computervisionsteknikker, herunder dyb læring og maskinlæringsalgoritmer. Den vurderer færdigheder i billedgenkendelse, objektdetektion, billedsegmentering og ekstraktion af funktion.

Covered skills:

  • Billedgenkendelse
  • Billedsegmentering
  • Konvolutionale neurale netværk
  • Billedklassificering
  • Maskinelæring
  • CV -rammer
  • Objektdetektion
  • Funktionsekstraktion
  • Neurale netværk
  • Dyb læring
  • Python

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9 reasons why
9 reasons why

Adaface Computer Vision Assessment Test is the most accurate way to shortlist Computer Vision Engineers



Reason #1

Tests for on-the-job skills

The Computer Vision 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 udføre billedgenkendelsesopgaver
  • Evne til at registrere objekter i billeder
  • Evne til nøjagtigt at segmentere billeder
  • Evne til at udtrække funktioner fra billeder
  • Evne til at arbejde med indviklede neurale netværk (CNN)
  • Evne til at opbygge neurale netværk til computervisionsopgaver
  • Evne til at klassificere billeder ved hjælp af maskinlæringsteknikker
  • Evne til at anvende dybe læringsprincipper på computervision
  • Evne til at anvende maskinlæringsalgoritmer til computersynsproblemer
  • Evne til at kode i Python til computervisionsopgaver
  • Kendskab til forskellige computervisionsrammer
Reason #2

No trick questions

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 Computervisionstest vil være ikke-gåbart.

🧐 Question

Medium

Changed decision boundary
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We have trained a model on a linearly separable training set to classify the data points into 2 sets (binary classification). Our intern recently labelled some new data points which are all correctly classified by the model. All of the new data points lie far away from the decision boundary. We added these new data points and re-trained our model- our decision boundary changed. Which of these models do you think we could be working with?
The 2 data sources use SQL Server and have a 3-character CompanyCode column. Both data sources contain an ORDER BY clause to sort the data by CompanyCode in ascending order. 

Teylor wants to make sure that the Merge Join transformation works without additional transformations. What would you recommend?
A: Perceptron
B: SVM
C: Logistic regression
D: Guassion discriminant analysis

Medium

CNN Architecture Tuning
Convolutional Neural Networks
Hyperparameter Optimization
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You are fine-tuning a Convolutional Neural Network (CNN) for image classification. The network architecture is as follows:
 image
The model is trained using the following parameters:

- Batch size: 64
- Learning rate: 0.001
- Optimizer: Adam
- Loss function: Categorical cross-entropy

After several training epochs, you observe that the training accuracy is high, but the validation accuracy plateaus and is significantly lower. This suggests possible overfitting. Which of the following adjustments would most effectively mitigate this issue without overly compromising the model's performance?
A: Increase the batch size to 128
B: Add dropout layers with a dropout rate of 0.5 after each MaxPooling2D layer
C: Replace Adam optimizer with SGD (Stochastic Gradient Descent)
D: Decrease the number of filters in each Conv2D layer by half
E: Increase the learning rate to 0.01
F: Reduce the size of the Dense layer to 64 units

Medium

CNN for Imbalanced Image Dataset
Convolutional Neural Networks
Imbalanced Datasets
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You are fine-tuning a Convolutional Neural Network (CNN) for an image classification task where the dataset is highly imbalanced. The majority class comprises 70% of the data. The initial model setup and subsequent experiments yield the following observations:

**Initial Setup:**

- CNN architecture: 6 convolutional layers with increasing filter sizes, followed by 2 fully connected layers.
- Activation function: ReLU
- No class-weighting or data augmentation.
- Results: High overall accuracy, but poor precision and recall for minority classes.

**Experiment 1:**

- Changes: Implement class-weighting to penalize mistakes on minority classes more heavily.
- Results: Improved precision and recall for minority classes, but overall accuracy slightly decreased.

**Experiment 2:**

- Changes: Add dropout layers with a rate of 0.5 after each convolutional layer.
- Results: Overall accuracy decreased, and no significant change in precision and recall for minority classes.

Given these outcomes, what is the most effective strategy to further improve the model's performance specifically for the minority classes without compromising the overall accuracy?
A: Increase the dropout rate to 0.7
B: Further fine-tune class-weighting parameters
C: Increase the number of filters in the convolutional layers
D: Add batch normalization layers after each convolutional layer
E: Use a different activation function like LeakyReLU
F: Implement more aggressive data augmentation on the minority class

Easy

Gradient descent optimization
Gradient Descent
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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:
 image
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
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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:
 image
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
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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?
 image

Easy

Recommendation System Selection
Recommender Systems
Collaborative Filtering
Content-Based Filtering
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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
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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:
 image
Based on the confusion matrix, what are the sensitivity and specificity of the model?

Medium

ZeroDivisionError and IndexError
Exceptions
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What will the following Python code output?
 image

Medium

Session
File Handling
Dictionary
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 image
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?
 image

Medium

Max Code
Arrays
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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:
 image

Medium

Recursive Function
Recursion
Dictionary
Lists
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Consider the following Python code:
 image
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
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What does the below function ‘fun’ does?
 image
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.
🧐 Question🔧 Skill

Medium

Changed decision boundary

2 mins

Deep Learning
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Medium

CNN Architecture Tuning
Convolutional Neural Networks
Hyperparameter Optimization

3 mins

Deep Learning
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Medium

CNN for Imbalanced Image Dataset
Convolutional Neural Networks
Imbalanced Datasets

3 mins

Deep Learning
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Easy

Gradient descent optimization
Gradient Descent

2 mins

Machine Learning
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Medium

Less complex decision tree model
Model Complexity
Overfitting

2 mins

Machine Learning
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Easy

n-gram generator

2 mins

Machine Learning
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Easy

Recommendation System Selection
Recommender Systems
Collaborative Filtering
Content-Based Filtering

2 mins

Machine Learning
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Easy

Sensitivity and Specificity
Confusion Matrix
Model Evaluation

2 mins

Machine Learning
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Medium

ZeroDivisionError and IndexError
Exceptions

2 mins

Python
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Medium

Session
File Handling
Dictionary

2 mins

Python
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Medium

Max Code
Arrays

2 mins

Python
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Medium

Recursive Function
Recursion
Dictionary
Lists

3 mins

Python
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Medium

Stacking problem
Stack
Linkedlist

4 mins

Python
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🧐 Question🔧 Skill💪 Difficulty⌛ Time
Changed decision boundary
Deep Learning
Medium2 mins
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CNN Architecture Tuning
Convolutional Neural Networks
Hyperparameter Optimization
Deep Learning
Medium3 mins
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CNN for Imbalanced Image Dataset
Convolutional Neural Networks
Imbalanced Datasets
Deep Learning
Medium3 mins
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Gradient descent optimization
Gradient Descent
Machine Learning
Easy2 mins
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Less complex decision tree model
Model Complexity
Overfitting
Machine Learning
Medium2 mins
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n-gram generator
Machine Learning
Easy2 mins
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Recommendation System Selection
Recommender Systems
Collaborative Filtering
Content-Based Filtering
Machine Learning
Easy2 mins
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Sensitivity and Specificity
Confusion Matrix
Model Evaluation
Machine Learning
Easy2 mins
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ZeroDivisionError and IndexError
Exceptions
Python
Medium2 mins
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Session
File Handling
Dictionary
Python
Medium2 mins
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Max Code
Arrays
Python
Medium2 mins
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Recursive Function
Recursion
Dictionary
Lists
Python
Medium3 mins
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Stacking problem
Stack
Linkedlist
Python
Medium4 mins
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Reason #4

1200+ customers in 75 countries

customers in 75 countries
Brandon

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 Computervisionstest 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 Computervisionstest 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.

invite candidates
Reason #7

Detailed scorecards & benchmarks

Se prøvescorekort
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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.

test completion rate
Reason #9

Advanced Proctoring


Learn more

About the Computer Vision Online Test

Why you should use Pre-employment Computer Vision Test?

The Computervisionstest 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:

  • Erfaring med at udvikle computervisionsapplikationer
  • Stærk viden om maskinlæringsalgoritmer
  • Ekspertise i Python -programmeringssprog
  • Dygtige til at bruge computervisionsrammer
  • Evne til at udføre billedgenkendelsesopgaver
  • Forståelse af objektdetekteringsteknikker
  • Kendskab til billedsegmenteringsmetoder
  • Erfaring med funktionsekstraktion fra billeder
  • Fortrolighed med indviklede neurale netværk
  • Færdighed i neurale netværksarkitekturer

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 Computer Vision Test?

  • objektdetektion </H4> <p> Objektdetektion er en computervisionsopgave, der involverer at finde og lokalisere objekter i billeder eller videoer. Denne færdighed måles i testen for at evaluere en kandidats viden om algoritmer og metoder, der bruges til at detektere og lokalisere objekter, hvilket er vigtigt i applikationer som overvågning, autonome køretøjer og billedbaserede søgesystemer. </p> <h4> billedsegmentering

    billedsegmentering er processen med at opdele et billede i flere regioner eller segmenter med det mål at forenkle eller analysere billedets repræsentation. Måling af denne færdighed i testen giver rekrutterere mulighed for at vurdere en kandidats evne til at bruge teknikker og algoritmer til billedsegmentering, som spiller en afgørende rolle i applikationer som medicinsk billedanalyse, genkendelse af objekt og billedredigering.

  • funktionsudtræk

    Funktionsekstraktion involverer at få meningsfuld information eller funktioner fra rå data, såsom billeder, for at lette efterfølgende analyse eller klassificering. Denne færdighed måles i testen for at evaluere en kandidats forståelse af funktionsekstraktionsteknikker, der bruges i computervision, som er afgørende for opgaver som objektgenkendelse, billedmatchning og mønsteranalyse.

  • konvolutionale neurale netværk </H4 > <p> Konvolutionale neurale netværk (CNN'er) er dybe læringsmodeller, der er specifikt designet til behandling af visuelle data, såsom billeder. Denne færdighed måles i testen for at vurdere en kandidats viden om CNN -arkitekturer samt deres evne til at træne og anvende CNN'er til opgaver som billedklassificering, objektdetektion og billedsegmentering. </p> <H4> neurale netværk <///// H4> <p> Neurale netværk er beregningsmodeller inspireret af strukturen og funktionen af ​​den menneskelige hjerne, der bruges til mønstergenkendelse og maskinlæringsopgaver. Måling af denne færdighed i testen giver rekrutterere mulighed for at evaluere en kandidats forståelse af neurale netværkskoncepter og deres evne til at anvende neurale netværk til løsning af computersynsproblemer. </p> <h4> billedklassificering

    Billedklassificering er Opgave til at tildele en etiket eller kategori til et billede baseret på dets indhold. Denne færdighed måles i testen for at vurdere en kandidats viden om klassificeringsalgoritmer og teknikker, der anvendes til billeder, som er vigtige for forskellige applikationer som billedsøgning, indholdsfiltrering og automatiseret billedmærkning.

  • dyb læring </////// H4> <p> Deep Learning er et underfelt af maskinlæring, der fokuserer på at opbygge og uddanne kunstige neurale netværk med flere lag. Måling af denne færdighed i testen giver rekrutterere mulighed for at evaluere en kandidats forståelse af dybe læringsprincipper og deres evne til at anvende dybe læringsmodeller på opgaver som billedgenkendelse, objektdetektion og billedgenerering. </p> <h4> Machine Learning </h4 > <p> Machine Learning er en gren af ​​kunstig intelligens, der fokuserer på at udvikle algoritmer og modeller, der er i stand til at lære af og foretage forudsigelser eller beslutninger baseret på data. Denne færdighed måles i testen for at vurdere en kandidats forståelse af maskinlæringskoncepter og deres evne til at anvende maskinlæringsteknikker til computersynsproblemer. </p> <h4> python

    python er et populært programmeringssprog Verligt brugt inden for computervision og maskinlæring. Måling af denne færdighed i testen giver rekrutterere mulighed for at evaluere en kandidats færdighed i Python -programmering samt deres evne til at implementere computervisionsalgoritmer og modeller ved hjælp af Python -biblioteker og rammer.

  • 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 Computervisionstest to be based on.

    Billedgenkendelse
    Objektdetektion
    Billedsegmentering
    Funktionsekstraktion
    Convolutional Neural Networks (CNN)
    Neurale netværk
    Billedklassificering
    Dyb læring
    Maskinelæring
    Python
    CV -rammer
    Forarbejdning
    Aktiveringsfunktioner
    Tabsfunktioner
    Optimeringsalgoritmer
    Dataforstørrelse
    Overføring af læring
    Backpropagation
    Regularisering
    Hyperparameterindstilling
    Krydsvalidering
    Binær klassificering
    Klassificering af flere klasse
    Objektlokalisering
    Bounding Box Regression
    Forekomstsegmentering
    Semantisk segmentering
    Encoder-decoder-arkitektur
    Gentagne neurale netværk (RNN)
    Indviklede lag
    Pooling lag
    Fuldt tilsluttede lag
    Batch -normalisering
    Droppe ud
    Billedforarbejdning
    Dataforstørrelsesteknikker
    Datamærkning
    Funktionsvalg
    Hovedkomponentanalyse (PCA)
    Lineær regression
    Logistisk regression
    Support Vector Machines (SVM)
    Tilfældige skove
    K-nærmeste naboer (KNN)
    Naive Bayes
    Modelevalueringsmålinger
    Forvirringsmatrix
    Præcision og tilbagekaldelse
    F1 score
    Modtagerens driftskarakteristik (ROC) kurve
    AUC-ROC-score
    Gittersøgning
    K-fold krydsvalidering
    Ensemble -læring
    Overfitting og underfitting
    Python Syntax
    Variable typer
    Kontrolstrøm
    Funktioner
    Filhåndtering
    Python -biblioteker
    Numpy
    Pandas
    Matplotlib
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What roles can I use the Computer Vision Test for?

  • Computer Vision Engineer
  • Machine Learning Engineer
  • AI -forsker
  • Dataforsker
  • Softwareudvikler
  • Dataanalytiker
  • Billedbehandlingsingeniør
  • Forsker
  • Computervisionskonsulent
  • Dataingeniør

How is the Computer Vision 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

  • Kapacitet i billedklassificering
  • Dyb forståelse af dybe læringskoncepter
  • Ekspertise inden for maskinlæringsalgoritmer
  • Dygtige til Python -programmeringssprog
  • Kapacitet til at arbejde med computervisionsrammer
  • Erfaring med implementering af billedgenkendelsesmodeller
  • Stærkt greb om principper om objektdetektering
  • Kendskab til avancerede billedsegmenteringsteknikker
  • Færdigheder i funktionsekstraktionsmetoder
  • Dybdegående forståelse af indviklede neurale netværk
Singapore government logo

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

Computer Vision 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.

Jeg er kandidat. Kan jeg prøve en øvelsestest?

Nej. Desværre understøtter vi ikke praksisforsøg i øjeblikket. Du kan dog bruge vores eksempler på spørgsmål til praksis.

Hvad er omkostningerne ved at bruge denne test?

Du kan tjekke vores prisplaner.

Kan jeg få en gratis prøve?

Ja, du kan tilmelde dig gratis og forhåndsvise denne test.

Jeg flyttede lige til en betalt plan. Hvordan kan jeg anmode om en brugerdefineret vurdering?

Her er en hurtig guide til hvordan man anmoder om en brugerdefineret vurdering på adaface.

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