Why You Should Use This Machine Learning Test

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. The pre-employment machine learning assessment test screens candidates for the typical skills recruiters look for Machine Learning developer roles: Experience designing, developing, and researching Machine Learning models Deep understanding of fundamental data structures and data modeling Expertise in math, probability and statistics Ability to write robust code Experience with machine learning frameworks (like Keras, PyTorch, Tensorflow etc.) and libraries (like scikit-learn, Numpy, Pandas etc.) Ability to choose hardware for running an ML model with the needed latency The insights generated from this assessment can be used by recruiters and hiring managers to identify the best candidates for Machine Learning developer roles. Anti-cheating features enable you to be comfortable with conducting assessments online. The Machine Learning Assessment Test is ideal for helping recruiters identify which candidates have the skills to do well on the job.

Machine Learning Assessment Test

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
Classification
Overfitting
Support Vector Machines
Cross-Validation

Gradient Descent
Accuracy Matrix
Decision Trees
Bias and Variance

See all covered skills

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9 reasons why

Adaface Machine Learning Assessment Test is the most accurate way to shortlist Machine Learning Engineers

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.

The pre-employment machine learning assessment test screens candidates for the typical skills recruiters look for Machine Learning developer roles:

Experience designing, developing, and researching Machine Learning models
Deep understanding of fundamental data structures and data modeling
Expertise in math, probability and statistics
Ability to write robust code
Experience with machine learning frameworks (like Keras, PyTorch, Tensorflow etc.) and libraries (like scikit-learn, Numpy, Pandas etc.)
Ability to choose hardware for running an ML model with the needed latency

The insights generated from this assessment can be used by recruiters and hiring managers to identify the best candidates for Machine Learning developer roles. Anti-cheating features enable you to be comfortable with conducting assessments online. The Machine Learning Assessment Test is ideal for helping recruiters identify which candidates have the skills to do well on the job.

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.

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.

Ce ne sont qu'un petit échantillon de notre bibliothèque de plus de 10 000 questions. Les questions réelles à ce sujet Machine Learning Assessment Test ne sera pas 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: 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: 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
Medium 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?
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: 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	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	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
	Medium n-gram generator	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	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?

	🧐 Question	🔧 Skill	💪 Difficulty	⌛ Time
	Gradient descent optimization Gradient Descent	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	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	Machine Learning	Medium	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	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?

Reason #4

1200+ customers in 75 countries

Avec Adaface, nous avons pu optimiser notre processus de dépistage initial de plus de 75%, libérant un temps précieux pour les gestionnaires d'embauche et notre équipe d'acquisition de talents!

Brandon Lee, Chef des gens, 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.

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

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

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About Machine Learning Developer Roles

Machine learning (ML) is a subset of artificial intelligence (AI) that enables software programmes to grow increasingly effective at predicting outcomes without explicitly programming them to do so. Machine learning algorithms estimate new output values by using past data as input.

A machine learning (ML) developer is an expert in training models with data. Following that, the models are utilised to automate activities such as image classification, speech recognition, and market forecasting.

A machine learning developer creates a solution that is unique to each situation. The only way to get the best results is to thoroughly process the data and use the appropriate algorithm for the current situation.

Typical Machine Learning Developer/Engineer responsibilities include:

Understanding corporate objectives and creating models to assist them be accomplished, as well as measurements to track their success
Managing available resources such as hardware, data, and staff in order to meet deadlines
Analyzing the ML algorithms that may be utilised to tackle a particular problem and evaluating them based on their likelihood of success
Exploring and visualising data
Verifying and/or assuring data quality through data cleansing
Locating public datasets that might be utilised for training
Developing Validation Methodologies
Specifying the preprocessing or feature engineering that will be performed on a given dataset
Creating pipelines for data augmentation
Analyzing the model's flaws and devising solutions to overcome them

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

Machine Learning Engineer
Machine Learning Developer

What topics are covered in the Machine Learning Assessment Test?

Training Data & Test Data

Bias-Variance Tradeoff

Overfitting and Underfitting

Classification

Statistics

Feature Engineering

Probability

Data Preparation

Regularization

Cross-Validation

Uni-variate Analysis

Bi-variate Analysis

Multivariate Analysis

Inferential Statistical Analysis

Outliers

Scaling (Standardization, Normalization)

Pre-Model Building

Model Splitting

Supervised Learning Algorithms

Regression: Linear Regression, Logistic Regression

Decision Tree

K-Nearest Neighbors

Naive Bayes

Support Vector Machines

Unsupervised Learning Algorithms

Segmentation

K-Means Clustering

Agglomerative Hierarchical Clustering

Dimensionality Reduction

Principal Component Analysis

Model Validation

Hyper Parameter Tuning

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Les responsables du recrutement ont estimé que grâce aux questions techniques qu'ils posaient lors des entretiens du panel, ils ont pu dire quels candidats avaient de meilleurs scores et se différenciaient de ceux qui ne marquaient pas aussi bien. Ils sont très satisfait avec la qualité des candidats présélectionnés avec le dépistage de l'ADAFACE.

85%

réduction du temps de dépistage

Voir l'étude de cas complète

Machine Learning Online Test FAQs

À quel type de questions le test d'apprentissage automatique comprend-il?

Ce test d'apprentissage automatique avant l'emploi est composé de questions basées sur un scénario qui nécessitent que les candidats démontrent leur capacité à:

Préparer des données pour les algorithmes d'apprentissage automatique
Utiliser des algorithmes ML comme la régression logistique, les machines vectorielles de support, les arbres de décision et les forêts aléatoires pour la classification
Construire des algorithmes de clustering
Proposer l'algorithme le plus approprié pour un cas d'utilisation spécifique
Estimer les performances des algorithmes d'apprentissage

Ce test ou l'évaluation peut-il être utilisé pour des rôles de senior en ingénieur d'apprentissage automatique?

Pour les ingénieurs seniors de l'apprentissage automatique, vous pouvez demander un test personnalisé. Dans les 48 heures, nos experts en la matière personnaliseront l'évaluation conformément à votre description de poste et au niveau de l'ancienneté. Un test typique pour les rôles supérieurs, en plus des fondamentaux, le test se concentrera sur le test de la capacité d'un candidat à:

Projets de structure ML
Identifier les lacunes de divers algorithmes d'apprentissage automatique
Concevoir un processus de nettoyage des données et d'étiquetage des données
Sélectionnez les mesures d'évaluation appropriées pour améliorer les performances du modèle
Évaluer l'impact des performances matérielles sur les algorithmes d'apprentissage automatique

Puis-je combiner plusieurs compétences en une seule évaluation personnalisée?

Oui absolument. Les évaluations personnalisées sont configurées en fonction de votre description de poste et comprendront des questions sur toutes les compétences indispensables que vous spécifiez.

Avez-vous en place des fonctionnalités anti-chétion ou de proctorisation?

Nous avons les fonctionnalités anti-modification suivantes en place:

Questions non googléables
IP Proctoring
Proctoring Web
Proctoring webcam
Détection du plagiat
navigateur sécurisé

En savoir plus sur les fonctionnalités de Proctoring.

Comment interpréter les résultats des tests?

La principale chose à garder à l'esprit est qu'une évaluation est un outil d'élimination, pas un outil de sélection. Une évaluation des compétences est optimisée pour vous aider à éliminer les candidats qui ne sont pas techniquement qualifiés pour le rôle, il n'est pas optimisé pour vous aider à trouver le meilleur candidat pour le rôle. Ainsi, la façon idéale d'utiliser une évaluation consiste à décider d'un score de seuil (généralement 55%, nous vous aidons à bencher) et à inviter tous les candidats qui marquent au-dessus du seuil pour les prochains cycles d'entrevue.

Pour quel niveau d'expérience puis-je utiliser ce test?

Chaque évaluation ADAFACE est personnalisée à votre description de poste / Persona de candidats idéaux (nos experts en la matière choisiront les bonnes questions pour votre évaluation de notre bibliothèque de 10000+ questions). Cette évaluation peut être personnalisée pour tout niveau d'expérience.

Chaque candidat reçoit-il les mêmes questions?

Oui, cela vous permet de comparer les candidats. Les options pour les questions du MCQ et l'ordre des questions sont randomisées. Nous avons Anti-Cheating / Proctoring en place. Dans notre plan d'entreprise, nous avons également la possibilité de créer plusieurs versions de la même évaluation avec des questions de niveaux de difficulté similaires.

Je suis candidat. Puis-je essayer un test de pratique?

Non. Malheureusement, nous ne soutenons pas les tests de pratique pour le moment. Cependant, vous pouvez utiliser nos exemples de questions pour la pratique.