Why you should use Machine Learning Assessment Test?

The Machine Learning 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: Implementing linear regression models for predictive analytics Applying gradient descent algorithm for model optimization Identifying and mitigating overfitting and underfitting issues in machine learning models Utilizing support vector machines for classification tasks Understanding the concepts of bias and variance in machine learning models Performing cross-validation to assess the performance of supervised learning models Applying various techniques in unsupervised learning such as clustering Implementing dimensionality reduction methods to improve model efficiency Evaluating machine learning models using appropriate evaluation metrics Utilizing feature engineering techniques to enhance model performance

Machine Learning Assessment Test

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

Linear Regression Gradient Descent Overfitting and underfitting Support Vector Machines Bias and Variance Cross-Validation Supervised Learning Unsupervised Learning Clustering Dimensionality Reduction Model Evaluation Feature Engineering

About the test:

The pre-employment machine learning assessment test evaluates a candidate's understanding of machine learning fundamentals like feature engineering, regression, variance, conditional probability, clustering, decision trees, nearest neighbors, Naïve Bayes, bias and overfitting. The test also assesses them on their ability to collect and prepare the dataset, train a model, evaluate the model, and iteratively improve the model's performance.

Covered skills:

Linear Regression
Overfitting and underfitting
Bias and Variance
Supervised Learning
Clustering
Model Evaluation

Gradient Descent
Support Vector Machines
Cross-Validation
Unsupervised Learning
Dimensionality Reduction
Feature Engineering

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9 reasons why

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

Reason #1

Tests for on-the-job skills

The Machine Learning Assessment Test helps recruiters and hiring managers identify qualified candidates from a pool of resumes, and helps in taking objective hiring decisions. It reduces the administrative overhead of interviewing too many candidates and saves time by filtering out unqualified candidates at the first step of the hiring process.

The test screens for the following skills that hiring managers look for in candidates:

Able to implement and understand Linear Regression algorithms
Proficient in Gradient Descent optimization algorithms
Familiar with the concepts of Overfitting and underfitting in machine learning models
Able to apply Support Vector Machines for classification tasks
Capable of recognizing and managing Bias and Variance in machine learning models
Skilled in Cross-Validation techniques for model evaluation
Experienced in Supervised Learning algorithms
Knowledgeable in Unsupervised Learning algorithms
Competent in performing Clustering tasks
Able to apply Dimensionality Reduction techniques
Proficient in evaluating machine learning models
Skilled in performing Feature Engineering for improving model performance

Reason #2

No trick questions

Traditional assessment tools use trick questions and puzzles for the screening, which creates a lot of frustration among candidates about having to go through irrelevant screening assessments.

View sample questions

The main reason we started Adaface is that traditional pre-employment assessment platforms are not a fair way for companies to evaluate candidates. At Adaface, our mission is to help companies find great candidates by assessing on-the-job skills required for a role.

Why we started Adaface

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Reason #3

Non-googleable questions

We have a very high focus on the quality of questions that test for on-the-job skills. Every question is non-googleable and we have a very high bar for the level of subject matter experts we onboard to create these questions. We have crawlers to check if any of the questions are leaked online. If/ when a question gets leaked, we get an alert. We change the question for you & let you know.

How we design questions

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

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

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

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

Reason #4

1200+ customers in 75 countries

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 Machine Learning Assessment 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 Machine Learning Assessment 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.

Reason #7

Detailed scorecards & benchmarks

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Reason #8

High completion rate

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

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

Reason #9

Advanced Proctoring

Learn more

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About the Machine Learning Online Test

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

The Machine Learning 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:

Implementing linear regression models for predictive analytics
Applying gradient descent algorithm for model optimization
Identifying and mitigating overfitting and underfitting issues in machine learning models
Utilizing support vector machines for classification tasks
Understanding the concepts of bias and variance in machine learning models
Performing cross-validation to assess the performance of supervised learning models
Applying various techniques in unsupervised learning such as clustering
Implementing dimensionality reduction methods to improve model efficiency
Evaluating machine learning models using appropriate evaluation metrics
Utilizing feature engineering techniques to enhance model performance

Once the test is sent to a candidate, the candidate receives a link in email to take the test. For each candidate, you will receive a detailed report with skills breakdown and benchmarks to shortlist the top candidates from your pool.

What topics are covered in the Machine Learning Assessment Test?

Linear Regression
Linear regression is a statistical modeling technique that aims to establish a linear relationship between the dependent variable and one or more independent variables. It is measured in this test to assess the candidate's understanding of basic regression concepts and their ability to apply linear regression models in solving real-world problems.
Gradient Descent
Gradient descent is an optimization algorithm widely used in machine learning to minimize the cost function of a model. It iteratively adjusts the model's parameters in the direction of steepest descent to find the optimal solution. Measuring this skill helps evaluate a candidate's proficiency in implementing and optimizing machine learning models through gradient-based methods.
Overfitting and Underfitting
Overfitting occurs when a machine learning model fits the training data too closely, leading to poor generalization and performance on unseen data. Underfitting, on the other hand, happens when the model is too simple and fails to capture the underlying patterns in the data. Assessing a candidate's understanding of overfitting and underfitting helps gauge their knowledge in model complexity and their ability to find the right balance for optimal performance.
Support Vector Machines
Support Vector Machines (SVM) are supervised learning algorithms used for classification and regression tasks. They find an optimal hyperplane that separates different classes or predicts continuous values. Measurement of this skill helps recruiters evaluate the candidate's competence in utilizing SVMs and their ability to handle both linear and non-linear classification or regression problems.
Bias and Variance
Bias refers to the error introduced by a model's overly simplistic assumptions, while variance measures the model's sensitivity to fluctuations in the training data. These two concepts help in understanding the trade-off between underfitting and overfitting. Evaluating a candidate's knowledge of bias and variance enables recruiters to assess their understanding of model performance and the ability to fine-tune models for better results.
Cross-Validation
Cross-validation is a technique used to assess the performance and generalization capabilities of machine learning models. It involves splitting the data into multiple subsets for training and testing, enabling a more robust evaluation of a model's performance. Evaluating a candidate's knowledge of cross-validation helps determine their expertise in model evaluation and their ability to avoid over-optimistic performance estimates.
Supervised Learning
Supervised learning is a machine learning task where a model learns from labeled data to make predictions or classifications. It involves having a clear target variable that the model aims to predict. Assessing this skill helps gauge a candidate's understanding of supervised learning algorithms and their ability to apply them to various prediction tasks.
Unsupervised Learning
Unsupervised learning is a machine learning task where a model learns from unlabeled data to find patterns or structures without specific target variables. This skill measures a candidate's familiarity with unsupervised learning algorithms, such as clustering and dimensionality reduction, and their ability to extract meaningful insights from unstructured data.
Clustering
Clustering is an unsupervised learning technique that groups similar data points together based on their characteristics or similarities. It helps identify natural structures or categories within data. Evaluating a candidate's knowledge of clustering algorithms signifies their proficiency in exploring patterns within data and their ability to segment datasets into meaningful clusters for further analysis.
Dimensionality Reduction
Dimensionality reduction is the process of reducing the number of input variables/features in machine learning models. It helps simplify complex datasets by removing redundant or irrelevant features while retaining essential information. Assessing this skill allows recruiters to evaluate a candidate's understanding of feature selection techniques and their ability to improve model performance and interpretability.
Model Evaluation
Model evaluation is the process of assessing the performance and quality of machine learning models. It involves using various metrics and techniques to measure how well a model generalizes to unseen data. Evaluating this skill helps recruiters determine a candidate's proficiency in evaluating and comparing different models and their ability to select the most appropriate one for a given task.
Feature Engineering
Feature engineering is the process of creating new features or transforming existing ones to improve the performance of machine learning models. It involves selecting, creating, or modifying variables to better represent the underlying patterns in the data. Measuring this skill enables recruiters to assess a candidate's expertise in enhancing the predictive power of models through insightful feature engineering techniques.
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 Machine Learning Assessment Test to be based on.
Linear Regression
Ordinary Least Squares
Gradient Descent
Stochastic Gradient Descent
Batch Gradient Descent
Ridge Regression
Lasso Regression
Polynomial Regression
Regularization
Overfitting
Underfitting
Support Vector Machines
Kernel Tricks
Hyperplane
Soft Margin
Hard Margin
Bias
Variance
Cross-Validation
K-Fold Cross-Validation
Leave-One-Out Cross-Validation
Holdout Method
Supervised Learning
Classification
Regression
Decision Trees
Random Forests
Naive Bayes
K-Nearest Neighbors
Neural Networks
Unsupervised Learning
Clustering
K-Means
Hierarchical
DBSCAN
Dimensionality Reduction
PCA (Principal Component Analysis)
LDA (Linear Discriminant Analysis)
t-SNE (t-Distributed Stochastic Neighbor Embedding)
Model Evaluation
Accuracy
Precision
Recall
F1 Score
ROC Curve
AUC (Area Under the Curve)
Confusion Matrix
Feature Engineering
Data Transformation
Feature Scaling
Dummy Variables
Variable Interactions
Handling Missing Data
Outlier Detection

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

Machine Learning Developer
Machine Learning Engineer
Data Scientist
Data Analyst
Artificial Intelligence Engineer
Data Engineer
Business Analyst
Research Scientist
Statistical Analyst
Data Mining Specialist

How is the Machine Learning Assessment Test customized for senior candidates?

For intermediate/ experienced candidates, we customize the assessment questions to include advanced topics and increase the difficulty level of the questions. This might include adding questions on topics like

Implementing decision trees and random forests for classification tasks
Applying ensemble methods such as bagging and boosting to improve model performance
Understanding the concepts and applications of neural networks
Implementing deep learning models for complex tasks
Utilizing natural language processing techniques for text classification and sentiment analysis
Applying recommendation systems for personalized recommendations
Understanding the concepts and applications of reinforcement learning
Implementing time series analysis for forecasting future trends
Utilizing anomaly detection techniques to identify unusual patterns in data
Applying transfer learning to leverage knowledge from pre-trained models

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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

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Machine Learning Hiring Test FAQs

What type of questions does the Machine Learning Test include?

This pre-employment machine learning test is comprised of scenario-based questions that require candidates to demonstrate their ability to:

Prepare data for machine learning algorithms
Use ML algorithms like logistic regression, support vector machines, decision trees and random forests for classification
Build clustering algorithms
Propose the most appropriate algorithm for a specific use case
Estimate performance of learning algorithms

Can this test or assessment be used for senior machine learning engineer roles?

For Senior machine learning engineers, you can request a custom test. Within 48 hours our subject matter experts will customize the assessment in accordance with your job description and seniority level. A typical test for senior roles, in addition to fundamentals the test will focus on testing a candidate's ability to:

Structure ML projects
Identify shortcomings of various machine learning algorithms
Design a data cleaning and data labelling process
Select proper evaluation metrics to improve model performance
Assess the impact of hardware performance on the machine learning algorithms

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.