Data Science Assessment Test

Q: What are the main Data Science-related tests?

Machine Learning Assessment Test Data Analysis Test Statistics Test

The data science assessment test evaluates a candidate's proficiency in statistics, probability, linear & non-linear regression models and their ability to analyze data and leverage Python/ R to extract insights from the data.

Covered skills:

Machine Learning Techniques
Data Visualization
Analytics with R or Python
Exploratory Data Analysis
Data Manipulation
Statistics
Regression Analysis
Data Cleansing
Predictive Modeling

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About the Data Science Test

The Data Science 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:

Ability to apply probability concepts and principles in data analysis
Ability to analyze and interpret statistical data
Ability to implement machine learning algorithms and techniques
Ability to visualize and present data effectively
Ability to perform data analysis and exploration using R or Python
Ability to manipulate and transform data efficiently
Ability to understand and apply statistical concepts in regression analysis
Ability to clean and preprocess data for analysis
Ability to develop predictive models for various data scenarios

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Use Adaface tests trusted by recruitment teams globally. Adaface skill assessments measure on-the-job skills of candidates, providing employers with an accurate tool for screening potential hires.

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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 15,000+ questions. The actual questions on this Data Science Assessment Test will be non-googleable.

🧐 Question
Medium Amazon electronics product feedback Data Imputation Handling Missing Values	Solve
Amazon's electronics store division has over the last few months focused on getting customer feedback on their products, and marking them as safe/ unsafe. Their data science team has used decision trees for this. The training set has these features: product ID, data, summary of feedback, detailed feedback and a binary safe/unsafe tag. During training, the data science team dropped any feedback records with missing features. The test set has a few records with missing "detailed feedback" field. What would you recommend? A: Remove the test samples with missing detailed feedback text fields B: Generate synthetic data to fill in missing fields C: Use an algorithm that handles missing data better than decision trees D: Fill in the missing detailed feedback text field with the summary of feedback field.
Easy Fraud detection model Logistic Regression Handling Class Imbalance False Negative Reduction	Solve
Your friend T-Rex is working on a logistic regression model for a bank, for a fraud detection usecase. The accuracy of the model is 98%. T-Rex's manager's concern is that 85% of fraud cases are not being recognized by the model. Which of the following will surely help the model recognize more than 15% of fraud cases?
Medium Rox's decision tree classifier Decision Tree Classifier Model Evaluation Overfitting Detection	Solve
Your data science intern Rox was asked to create a decision tree classifier with 12 input variables. The tree used 7 of the 12 variables, and was 5 levels deep. Few nodes of the tree contain 3 data points. The area under the curve (AUC) is 0.86. As Rox's mentor, what is your interpretation? A. The AUC is high, and the small nodes are all very pure- the model looks accurate. B. The tree might be overfitting- try fitting shallower trees and using an ensemble method. C. The AUC is high, so overall the model is accurate. It might not be well-calibrated, because the small nodes will give poor estimates of probability. D. The tree did not split on all the input variables. We need a larger data set to get a more accurate model.
Easy Gradient descent optimization Gradient Descent Learning Rate Schedules Optimization Techniques	Solve
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 Data Transformation Overfitting Prevention	Solve
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 String Manipulation Algorithm	Solve
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	Solve
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 Sensitivity Specificity	Solve
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?
Medium Green or red balls Probability Combinatorics	Solve
A bag contains 5 red balls, 6 yellow balls and 3 green balls. If two balls are picked at random, what is the probability that both are red or both are green in colour?
Hard Square points and Circle Probability Geometry	Solve
What is the probability that two uniformly random points in the square are such that center of the square lies in the circle formed by taking the points as diameter
Easy Frequency distribution	Solve
Convert the following into an ordinary frequency distribution: - 5 users gave less than 3 rating - 12 users gave less than 6 rating - 25 users gave less than 9 ratings - 33 users get less than 12 ratings

	🧐 Question	🔧 Skill
	Medium Amazon electronics product feedback Data Imputation Handling Missing Values	2 mins Data Science	Solve
Amazon's electronics store division has over the last few months focused on getting customer feedback on their products, and marking them as safe/ unsafe. Their data science team has used decision trees for this. The training set has these features: product ID, data, summary of feedback, detailed feedback and a binary safe/unsafe tag. During training, the data science team dropped any feedback records with missing features. The test set has a few records with missing "detailed feedback" field. What would you recommend? A: Remove the test samples with missing detailed feedback text fields B: Generate synthetic data to fill in missing fields C: Use an algorithm that handles missing data better than decision trees D: Fill in the missing detailed feedback text field with the summary of feedback field.
	Easy Fraud detection model Logistic Regression Handling Class Imbalance False Negative Reduction	2 mins Data Science	Solve
Your friend T-Rex is working on a logistic regression model for a bank, for a fraud detection usecase. The accuracy of the model is 98%. T-Rex's manager's concern is that 85% of fraud cases are not being recognized by the model. Which of the following will surely help the model recognize more than 15% of fraud cases?
	Medium Rox's decision tree classifier Decision Tree Classifier Model Evaluation Overfitting Detection	2 mins Data Science	Solve
Your data science intern Rox was asked to create a decision tree classifier with 12 input variables. The tree used 7 of the 12 variables, and was 5 levels deep. Few nodes of the tree contain 3 data points. The area under the curve (AUC) is 0.86. As Rox's mentor, what is your interpretation? A. The AUC is high, and the small nodes are all very pure- the model looks accurate. B. The tree might be overfitting- try fitting shallower trees and using an ensemble method. C. The AUC is high, so overall the model is accurate. It might not be well-calibrated, because the small nodes will give poor estimates of probability. D. The tree did not split on all the input variables. We need a larger data set to get a more accurate model.
	Easy Gradient descent optimization Gradient Descent Learning Rate Schedules Optimization Techniques	2 mins Machine Learning	Solve
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 Data Transformation Overfitting Prevention	2 mins Machine Learning	Solve
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 String Manipulation Algorithm	2 mins Machine Learning	Solve
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	Solve
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 Sensitivity Specificity	2 mins Machine Learning	Solve
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?
	Medium Green or red balls Probability Combinatorics	2 mins Probability	Solve
A bag contains 5 red balls, 6 yellow balls and 3 green balls. If two balls are picked at random, what is the probability that both are red or both are green in colour?
	Hard Square points and Circle Probability Geometry	3 mins Probability	Solve
What is the probability that two uniformly random points in the square are such that center of the square lies in the circle formed by taking the points as diameter
	Easy Frequency distribution	3 mins Statistics	Solve
Convert the following into an ordinary frequency distribution: - 5 users gave less than 3 rating - 12 users gave less than 6 rating - 25 users gave less than 9 ratings - 33 users get less than 12 ratings

	🧐 Question	🔧 Skill	💪 Difficulty	⌛ Time
	Amazon electronics product feedback Data Imputation Handling Missing Values	Data Science	Medium	2 mins	Solve
Amazon's electronics store division has over the last few months focused on getting customer feedback on their products, and marking them as safe/ unsafe. Their data science team has used decision trees for this. The training set has these features: product ID, data, summary of feedback, detailed feedback and a binary safe/unsafe tag. During training, the data science team dropped any feedback records with missing features. The test set has a few records with missing "detailed feedback" field. What would you recommend? A: Remove the test samples with missing detailed feedback text fields B: Generate synthetic data to fill in missing fields C: Use an algorithm that handles missing data better than decision trees D: Fill in the missing detailed feedback text field with the summary of feedback field.
	Fraud detection model Logistic Regression Handling Class Imbalance False Negative Reduction	Data Science	Easy	2 mins	Solve
Your friend T-Rex is working on a logistic regression model for a bank, for a fraud detection usecase. The accuracy of the model is 98%. T-Rex's manager's concern is that 85% of fraud cases are not being recognized by the model. Which of the following will surely help the model recognize more than 15% of fraud cases?
	Rox's decision tree classifier Decision Tree Classifier Model Evaluation Overfitting Detection	Data Science	Medium	2 mins	Solve
Your data science intern Rox was asked to create a decision tree classifier with 12 input variables. The tree used 7 of the 12 variables, and was 5 levels deep. Few nodes of the tree contain 3 data points. The area under the curve (AUC) is 0.86. As Rox's mentor, what is your interpretation? A. The AUC is high, and the small nodes are all very pure- the model looks accurate. B. The tree might be overfitting- try fitting shallower trees and using an ensemble method. C. The AUC is high, so overall the model is accurate. It might not be well-calibrated, because the small nodes will give poor estimates of probability. D. The tree did not split on all the input variables. We need a larger data set to get a more accurate model.
	Gradient descent optimization Gradient Descent Learning Rate Schedules Optimization Techniques	Machine Learning	Easy	2 mins	Solve
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 Data Transformation Overfitting Prevention	Machine Learning	Medium	2 mins	Solve
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 String Manipulation Algorithm	Machine Learning	Easy	2 mins	Solve
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	Solve
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 Sensitivity Specificity	Machine Learning	Easy	2 mins	Solve
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?
	Green or red balls Probability Combinatorics	Probability	Medium	2 mins	Solve
A bag contains 5 red balls, 6 yellow balls and 3 green balls. If two balls are picked at random, what is the probability that both are red or both are green in colour?
	Square points and Circle Probability Geometry	Probability	Hard	3 mins	Solve
What is the probability that two uniformly random points in the square are such that center of the square lies in the circle formed by taking the points as diameter
	Frequency distribution	Statistics	Easy	3 mins	Solve
Convert the following into an ordinary frequency distribution: - 5 users gave less than 3 rating - 12 users gave less than 6 rating - 25 users gave less than 9 ratings - 33 users get less than 12 ratings

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Why you should use Pre-employment Data Science Assessment Test?

The Data Science Assessment 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:

Demonstrate a strong understanding of probability theory and its applications in data science.
Apply statistical concepts and techniques to analyze and interpret data.
Utilize machine learning algorithms and models to solve real-world problems.
Create visually appealing data visualizations to effectively communicate insights.
Employ R or Python programming languages for data analytics and manipulation.
Conduct comprehensive exploratory data analysis to gain insights and identify patterns.
Demonstrate proficiency in data manipulation techniques to clean and preprocess data.
Apply regression analysis to develop predictive models and make accurate predictions.
Possess advanced skills in data cleansing to ensure data quality and integrity.
Leverage predictive modeling techniques for making data-driven decisions.

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 Data Science Assessment Test?

Machine Learning Techniques: Machine learning techniques refer to the algorithms and methods used to train models that can automatically learn and improve from data without being explicitly programmed. This skill should be measured in the test as it is a fundamental component of data science, enabling data scientists to develop predictive models and make data-driven decisions.

Data Visualization: Data visualization involves creating visual representations of data to effectively communicate insights and patterns. This skill should be measured in the test as it is essential for data scientists to present complex data in a meaningful and understandable way, facilitating better decision-making and communication.

Analytics with R or Python: Analytics with R or Python refers to using programming languages such as R or Python to perform data analysis, statistical modeling, and machine learning tasks. This skill should be measured in the test as it assesses a candidate's ability to apply programming skills in data science projects, demonstrating their proficiency in handling data and implementing analytics algorithms.

Exploratory Data Analysis: Exploratory data analysis involves examining and transforming data to understand its main characteristics, patterns, and relationships. This skill should be measured in the test as it showcases a candidate's ability to extract meaningful insights from raw data, identify potential issues, and generate hypotheses for further analysis.

Data Manipulation: Data manipulation refers to the process of transforming, reformatting, or cleansing data to make it suitable for analysis. This skill should be measured in the test as it assesses a candidate's proficiency in handling and preparing data, which is a crucial step in the data science workflow before performing analytics or modeling tasks.

Statistics: Statistics involves the collection, analysis, interpretation, presentation, and organization of data. This skill should be measured in the test as it tests a candidate's understanding and application of statistical concepts and techniques, which are essential for conducting robust and valid data analysis.

Regression Analysis: Regression analysis is a statistical technique used to model the relationship between a dependent variable and one or more independent variables. This skill should be measured in the test as it evaluates a candidate's ability to perform regression analysis, which is widely used in predictive modeling and understanding the impact of variables on an outcome of interest.

Data Cleansing: Data cleansing involves identifying and correcting or removing errors, inconsistencies, or inaccuracies in datasets. This skill should be measured in the test as it assesses a candidate's capability to ensure data quality, which is crucial for obtaining reliable and accurate results in data analysis and modeling tasks.

Predictive Modeling: Predictive modeling is the process of developing and deploying mathematical models to predict future events or outcomes based on historical data. This skill should be measured in the test as it evaluates a candidate's ability to build predictive models using appropriate algorithms and evaluate their performance, which is a key component of many data science projects.

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 Data Science Assessment Test to be based on.

Probability distributions

Hypothesis testing

Central limit theorem

Confidence intervals

Linear regression

Logistic regression

Decision trees

Random forests

Support vector machines

k-nearest neighbors

Naive Bayes

K-means clustering

Hierarchical clustering

Principal component analysis

Data visualization techniques

Data visualization libraries (e.g., Matplotlib, ggplot)

Data exploration techniques

Exploratory data analysis

Data manipulation with R or Python

Data cleaning techniques

Missing data imputation

Outlier detection

Feature engineering

Correlation analysis

ANOVA

Time series analysis

A/B testing

Model evaluation and validation

Cross-validation techniques

Feature selection methods

Dimensionality reduction techniques

Ensemble learning

Overfitting and underfitting

Regularization techniques

Bias-variance tradeoff

Data preprocessing

Normalization

Standardization

One-hot encoding

Data scaling

Resampling methods

Data splitting techniques

Model evaluation metrics

R-squared

Mean squared error

Accuracy

Precision and recall

F1 score

ROC curve analysis

Hyperparameter tuning

Grid search

Cross-validation hyperparameter tuning

Model deployment

API integration

Model interpretation and explanation

Interpretable machine learning models

Shapley values

What roles can I use the Data Science Assessment Test for?

Data Scientist
Data Analyst
Machine Learning Engineer
Data Engineer
Business Analyst
Statistical Analyst
AI Engineer
Artificial Intelligence Roles

How is the Data Science 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

Utilize clustering algorithms for classification and segmentation analysis.
Apply time series analysis to forecast future trends and patterns.
Demonstrate knowledge of natural language processing algorithms and techniques.
Utilize feature selection and extraction techniques to improve model performance.
Employ dimensionality reduction methods for data visualization and analysis.
Apply ensemble learning techniques for improved model accuracy and performance.
Possess strong skills in data visualization using libraries such as Matplotlib and ggplot.
Utilize statistical testing and hypothesis testing to make data-driven decisions.
Employ data imputation techniques to handle missing values in datasets.
Apply cross-validation techniques to assess model performance and prevent overfitting.
Demonstrate expertise in handling imbalanced datasets using various techniques.

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Have questions about the Data Science Hiring Test?

What is the Data Science Assessment Test?

The Data Science Assessment Test evaluates candidates on their knowledge of data science concepts, covering skills such as Machine Learning techniques, Data Visualization, and Predictive Modeling. Recruiters use this test to efficiently screen applicants and identify top talent.

Can I combine the Data Science test with SQL questions?

Yes, you can request a custom test that includes both Data Science and SQL questions. For more details about our SQL assessment, please visit SQL Online Test.

What skills are assessed for senior data scientist roles?

Senior roles are evaluated on advanced competencies including clustering algorithms, time series analysis, natural language processing, feature selection and extraction, ensemble learning, data imputation, and handling imbalanced datasets.

How to use the Data Science Assessment Test in my hiring process?

We recommend using the test as a pre-screening tool. Add the assessment link in your job post or invite candidates via email. Adaface helps you identify skilled candidates early in the process.

Can I test Data Science and Machine Learning together in a test?

Yes, you can combine Data Science and Machine Learning questions into a single test. For more information, visit Machine Learning Assessment Test.

What are the main Data Science-related tests?

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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Along with scorecards that report the performance of the candidate in detail, you also receive a comparative analysis against the company average and industry standards.

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