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About the test:

The Computer Vision Test evaluates a candidate's knowledge and understanding of computer vision techniques, including deep learning and machine learning algorithms. It assesses skills in image recognition, object detection, image segmentation, and feature extraction.

Covered skills:

  • Image Recognition
  • Image Segmentation
  • Convolutional Neural Networks
  • Image Classification
  • Machine Learning
  • CV Frameworks
  • Object Detection
  • Feature Extraction
  • Neural Networks
  • Deep Learning
  • 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:

  • Ability to perform image recognition tasks
  • Ability to detect objects in images
  • Ability to accurately segment images
  • Ability to extract features from images
  • Ability to work with convolutional neural networks (CNN)
  • Ability to build neural networks for computer vision tasks
  • Ability to classify images using machine learning techniques
  • Ability to apply deep learning principles to computer vision
  • Ability to apply machine learning algorithms to computer vision problems
  • Ability to code in Python for computer vision tasks
  • Knowledge of various computer vision frameworks
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

These are just a small sample from our library of 10,000+ questions. The actual questions on this Computer Vision Test will be non-googleable.

🧐 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

With Adaface, we were able to optimise our initial screening process by upwards of 75%, freeing up precious time for both hiring managers and our talent acquisition team alike!


Brandon Lee, Head of People, Love, Bonito

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Reason #5

Designed for elimination, not selection

The most important thing while implementing the pre-employment Computer Vision 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 Computer Vision 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.

invite candidates
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.

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 Computer Vision 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:

  • Experience in developing computer vision applications
  • Strong knowledge of machine learning algorithms
  • Expertise in Python programming language
  • Proficient in using computer vision frameworks
  • Ability to perform image recognition tasks
  • Understanding of object detection techniques
  • Knowledge of image segmentation methods
  • Experience in feature extraction from images
  • Familiarity with convolutional neural networks
  • Proficiency in neural network architectures

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?

  • Image Recognition

    Image recognition is the ability of a computer system to identify and classify objects or patterns in digital images. This skill is measured in the test to assess a candidate's understanding of the fundamental concepts and techniques used in image recognition, which is crucial for various tasks such as object identification, image search, and content analysis.

  • Object Detection

    Object detection is a computer vision task that involves finding and localizing objects in images or videos. This skill is measured in the test to evaluate a candidate's knowledge of algorithms and methods used for detecting and locating objects, which is essential in applications like surveillance, autonomous vehicles, and image-based search systems.

  • Image Segmentation

    Image segmentation is the process of partitioning an image into multiple regions or segments, with the goal of simplifying or analyzing the image's representation. Measuring this skill in the test allows recruiters to assess a candidate's ability to use techniques and algorithms for image segmentation, which plays a crucial role in applications like medical image analysis, object recognition, and image editing.

  • Feature Extraction

    Feature extraction involves deriving meaningful information or features from raw data, such as images, to facilitate subsequent analysis or classification. This skill is measured in the test to evaluate a candidate's understanding of feature extraction techniques used in computer vision, which are crucial for tasks like object recognition, image matching, and pattern analysis.

  • Convolutional Neural Networks

    Convolutional Neural Networks (CNNs) are deep learning models specifically designed for processing visual data, such as images. This skill is measured in the test to assess a candidate's knowledge of CNN architectures, as well as their ability to train and apply CNNs for tasks like image classification, object detection, and image segmentation.

  • Neural Networks

    Neural networks are computational models inspired by the structure and function of the human brain, used for pattern recognition and machine learning tasks. Measuring this skill in the test allows recruiters to evaluate a candidate's understanding of neural network concepts and their ability to apply neural networks for solving computer vision problems.

  • Image Classification

    Image classification is the task of assigning a label or category to an image based on its content. This skill is measured in the test to assess a candidate's knowledge of classification algorithms and techniques applied to images, which are essential for various applications like image search, content filtering, and automated image tagging.

  • Deep Learning

    Deep learning is a subfield of machine learning that focuses on building and training artificial neural networks with multiple layers. Measuring this skill in the test allows recruiters to evaluate a candidate's understanding of deep learning principles and their ability to apply deep learning models to tasks like image recognition, object detection, and image generation.

  • Machine Learning

    Machine learning is a branch of artificial intelligence that focuses on developing algorithms and models capable of learning from and making predictions or decisions based on data. This skill is measured in the test to assess a candidate's understanding of machine learning concepts and their ability to apply machine learning techniques to computer vision problems.

  • Python

    Python is a popular programming language widely used in the field of computer vision and machine learning. Measuring this skill in the test allows recruiters to evaluate a candidate's proficiency in Python programming, as well as their ability to implement computer vision algorithms and models using Python libraries and frameworks.

  • CV Frameworks

    CV frameworks, or computer vision frameworks, are software libraries or platforms that provide ready-to-use tools and functions for developing computer vision applications. This skill is measured in the test to assess a candidate's familiarity with popular CV frameworks like OpenCV, TensorFlow, or PyTorch, which are essential for rapid prototyping, algorithm implementation, and deployment of computer vision solutions.

  • 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 Computer Vision Test to be based on.

    Image recognition
    Object detection
    Image segmentation
    Feature extraction
    Convolutional neural networks (CNN)
    Neural networks
    Image classification
    Deep learning
    Machine learning
    Python
    CV frameworks
    Preprocessing
    Activation functions
    Loss functions
    Optimization algorithms
    Data augmentation
    Transfer learning
    Backpropagation
    Regularization
    Hyperparameter tuning
    Cross-validation
    Binary classification
    Multi-class classification
    Object localization
    Bounding box regression
    Instance segmentation
    Semantic segmentation
    Encoder-decoder architecture
    Recurrent neural networks (RNN)
    Convolutional layers
    Pooling layers
    Fully connected layers
    Batch normalization
    Dropout
    Image preprocessing
    Data augmentation techniques
    Data labeling
    Feature selection
    Principal component analysis (PCA)
    Linear regression
    Logistic regression
    Support vector machines (SVM)
    Random forests
    K-nearest neighbors (KNN)
    Naive Bayes
    Model evaluation metrics
    Confusion matrix
    Precision and recall
    F1 score
    Receiver operating characteristic (ROC) curve
    AUC-ROC score
    Grid search
    K-fold cross-validation
    Ensemble learning
    Overfitting and underfitting
    Python syntax
    Variable types
    Control flow
    Functions
    File handling
    Python libraries
    NumPy
    Pandas
    Matplotlib
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What roles can I use the Computer Vision Test for?

  • Computer Vision Engineer
  • Machine Learning Engineer
  • AI Researcher
  • Data Scientist
  • Software Developer
  • Data Analyst
  • Image Processing Engineer
  • Research Scientist
  • Computer Vision Consultant
  • Data Engineer

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

  • Capability in image classification
  • Deep understanding of deep learning concepts
  • Expertise in machine learning algorithms
  • Proficient in Python programming language
  • Capability to work with computer vision frameworks
  • Experience in implementing image recognition models
  • Strong grasp of object detection principles
  • Knowledge of advanced image segmentation techniques
  • Proficiency in feature extraction methods
  • In-depth understanding of convolutional neural networks
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The hiring managers felt that through the technical questions that they asked during the panel interviews, they were able to tell which candidates had better scores, and differentiated with those who did not score as well. They are highly satisfied with the quality of candidates shortlisted with the Adaface screening.


85%
reduction in screening time

Computer Vision Hiring Test FAQs

Can I combine multiple skills into one custom assessment?

Yes, absolutely. Custom assessments are set up based on your job description, and will include questions on all must-have skills you specify. Here's a quick guide on how you can request a custom test.

Do you have any anti-cheating or proctoring features in place?

We have the following anti-cheating features in place:

  • Non-googleable questions
  • IP proctoring
  • Screen proctoring
  • Web proctoring
  • Webcam proctoring
  • Plagiarism detection
  • Secure browser
  • Copy paste protection

Read more about the proctoring features.

How do I interpret test scores?

The primary thing to keep in mind is that an assessment is an elimination tool, not a selection tool. A skills assessment is optimized to help you eliminate candidates who are not technically qualified for the role, it is not optimized to help you find the best candidate for the role. So the ideal way to use an assessment is to decide a threshold score (typically 55%, we help you benchmark) and invite all candidates who score above the threshold for the next rounds of interview.

What experience level can I use this test for?

Each Adaface assessment is customized to your job description/ ideal candidate persona (our subject matter experts will pick the right questions for your assessment from our library of 10000+ questions). This assessment can be customized for any experience level.

Does every candidate get the same questions?

Yes, it makes it much easier for you to compare candidates. Options for MCQ questions and the order of questions are randomized. We have anti-cheating/ proctoring features in place. In our enterprise plan, we also have the option to create multiple versions of the same assessment with questions of similar difficulty levels.

I'm a candidate. Can I try a practice test?

No. Unfortunately, we do not support practice tests at the moment. However, you can use our sample questions for practice.

What is the cost of using this test?

You can check out our pricing plans.

Can I get a free trial?

Yes, you can sign up for free and preview this test.

I just moved to a paid plan. How can I request a custom assessment?

Here is a quick guide on how to request a custom assessment on Adaface.

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