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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, NaΓ―ve 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

9 reasons why
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

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
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
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:
 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
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:
 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
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?
 image

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
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:
 image
Based on the confusion matrix, what are the sensitivity and specificity of the model?
🧐 QuestionπŸ”§ Skill

Easy

Gradient descent optimization
Gradient Descent

2 mins

Machine Learning
Solve

Medium

Less complex decision tree model
Model Complexity
Overfitting

2 mins

Machine Learning
Solve

Easy

n-gram generator

2 mins

Machine Learning
Solve

Easy

Recommendation System Selection
Recommender Systems
Collaborative Filtering
Content-Based Filtering

2 mins

Machine Learning
Solve

Easy

Sensitivity and Specificity
Confusion Matrix
Model Evaluation

2 mins

Machine Learning
Solve
🧐 QuestionπŸ”§ SkillπŸ’ͺ DifficultyβŒ› Time
Gradient descent optimization
Gradient Descent
Machine Learning
Easy2 mins
Solve
Less complex decision tree model
Model Complexity
Overfitting
Machine Learning
Medium2 mins
Solve
n-gram generator
Machine Learning
Easy2 mins
Solve
Recommendation System Selection
Recommender Systems
Collaborative Filtering
Content-Based Filtering
Machine Learning
Easy2 mins
Solve
Sensitivity and Specificity
Confusion Matrix
Model Evaluation
Machine Learning
Easy2 mins
Solve
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

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.

invite candidates
Reason #7

Detailed scorecards & benchmarks

View sample scorecard
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 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

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
Singapore government logo

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

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.

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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