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About the test:

The Neural Networks Test evaluates a candidate's knowledge and understanding of neural networks, deep learning, machine learning, Python, data science, and NumPy. It includes multiple-choice questions to assess theoretical knowledge and coding questions to evaluate programming skills in Python.

Covered skills:

  • Neural Networks Basics
  • Deep Neural Networks
  • Machine Learning
  • Data Science
  • Shallow Neural Networks
  • Deep Learning
  • Python
  • NumPy

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9 reasons why
9 reasons why

Adaface Neural Networks Assessment Test is the most accurate way to shortlist Data Scientists



Reason #1

Tests for on-the-job skills

The Neural Networks 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:

  • Understanding the basics of Neural Networks
  • Ability to implement Shallow Neural Networks
  • Knowledge of Deep Neural Networks architecture
  • Proficiency in Deep Learning concepts
  • Understanding of Machine Learning algorithms
  • Ability to write Python code for Neural Networks
  • Familiarity with Data Science principles
  • Proficiency in NumPy for data manipulation
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 Neural Networks 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.

Medium

Array Manipulation and Summation
Array Manipulation
Mathematical Operations
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Consider the following code snippet:
 image
What will be the value of G after executing the code?

Medium

Matrix Eigenvalues and Diagonalization
Linear Algebra
Matrix Operations
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Consider the following code snippet:
 image
After running this code, which of the following statements is true regarding the B matrix?
🧐 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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Medium

Array Manipulation and Summation
Array Manipulation
Mathematical Operations

2 mins

NumPy
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Medium

Matrix Eigenvalues and Diagonalization
Linear Algebra
Matrix Operations

3 mins

NumPy
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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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Array Manipulation and Summation
Array Manipulation
Mathematical Operations
NumPy
Medium2 mins
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Matrix Eigenvalues and Diagonalization
Linear Algebra
Matrix Operations
NumPy
Medium3 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 Neural Networks 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 Neural Networks 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 Neural Networks Online Test

Why you should use Pre-employment Neural Networks Test?

The Neural Networks 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:

  • Understanding the basics of Neural Networks
  • Implementing Shallow Neural Networks
  • Building Deep Neural Networks
  • Applying Deep Learning principles
  • Creating Machine Learning models
  • Using Python for Neural Networks
  • Applying Data Science concepts
  • Working with NumPy arrays
  • Implementing Neural Networks optimizations
  • Applying advanced Deep Learning techniques

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 Neural Networks Test?

  • Neural Networks Basics

    Neural Networks Basics refers to the fundamental concepts and principles of neural networks, including the structure, operation, and learning algorithms. This skill is measured in the test to evaluate the candidate's understanding of the foundation of neural networks and their ability to apply this knowledge in practical scenarios.

  • Shallow Neural Networks

    Shallow Neural Networks focus on neural networks with only one hidden layer. This skill assesses the candidate's understanding of designing and training simple neural networks for relatively straightforward tasks.

  • Deep Neural Networks

    Deep Neural Networks involve neural networks with multiple hidden layers. This skill evaluates the candidate's expertise in developing and optimizing complex neural networks to tackle more intricate problems that require hierarchical representation learning.

  • Deep Learning

    Deep Learning encompasses the broader field of using deep neural networks to learn and extract meaningful patterns from large, unstructured datasets. Measuring this skill assesses the candidate's ability to leverage deep learning techniques effectively and utilize state-of-the-art architectures and algorithms for real-world applications.

  • Machine Learning

    Machine Learning focuses on training algorithms and statistical models that enable computers to learn from and make predictions or decisions based on data. Measuring this skill helps evaluate the candidate's grasp of machine learning concepts, including feature engineering, model selection, and performance evaluation.

  • Python

    Python is a widely used programming language in data science and machine learning. This skill assesses the candidate's ability to write Python code to implement neural networks and apply various data manipulation and analysis techniques using libraries such as NumPy and Pandas.

  • Data Science

    Data Science encompasses the interdisciplinary field of extracting insights and knowledge from data through various scientific methods, algorithms, and processes. Measuring this skill evaluates the candidate's understanding of data pre-processing, visualization, feature extraction, and other essential aspects required for solving real-world problems.

  • NumPy

    NumPy is a fundamental library in Python for numerical computing and efficient handling of large multi-dimensional arrays and matrices. This skill measures the candidate's proficiency in utilizing NumPy for mathematical operations, linear algebra, and data manipulation tasks, which are crucial in building and training neural networks.

  • 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 Neural Networks Test to be based on.

    Activation functions
    Feedforward process
    Backpropagation algorithm
    Gradient descent
    Cost functions
    Regularization techniques
    Convolutional Neural Networks (CNN)
    Recurrent Neural Networks (RNN)
    Long Short-Term Memory (LSTM)
    Autoencoders
    Deep Belief Networks (DBN)
    Generative Adversarial Networks (GAN)
    Dropout regularization
    Transfer learning
    Hyperparameter tuning
    Image recognition
    Natural Language Processing (NLP)
    Object detection
    Overfitting and underfitting
    Support Vector Machines (SVM)
    Decision trees
    Random Forests
    k-Nearest Neighbors (k-NN)
    Linear regression
    Logistic regression
    k-Means clustering
    Principal Component Analysis (PCA)
    Evaluation metrics
    Cross-validation
    One-hot encoding
    Data cleaning
    Data preprocessing
    Scikit-learn library
    Pandas library
    Matplotlib library
    Data visualization
    Data analysis
    Python syntax
    Conditional statements
    Loops
    Functions
    List manipulation
    String manipulation
    File handling
    Exception handling
    Importing modules
    NumPy arrays
    Array manipulation
    Indexing and slicing
    Matrix operations
    Linear algebra
    Statistical functions
    Data type conversion
    Random number generation
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What roles can I use the Neural Networks Test for?

  • Data Scientist
  • Machine Learning Engineer
  • AI Researcher
  • Data Analyst
  • Python Developer
  • Data Engineer
  • Artificial Intelligence Specialist
  • Research Scientist
  • Big Data Engineer
  • Software Engineer

How is the Neural Networks 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

  • Utilizing Machine Learning algorithms
  • Using Python libraries for Neural Networks
  • Applying mathematical concepts in Deep Learning
  • Implementing Neural Networks architectures
  • Analyzing and visualizing Neural Networks results
  • Applying Neural Networks in real-world scenarios
  • Understanding Neural Networks regularization techniques
  • Optimizing Neural Networks hyperparameters
  • Applying transfer learning in Deep Learning
  • Designing and training Generative Adversarial 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

Neural Networks 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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