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 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.

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.

Questi sono solo un piccolo campione della nostra biblioteca di oltre 10.000 domande. Le domande reali su questo Machine Learning Assessment Test sarà non googleabile.

🧐 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
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?
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
	Easy 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	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	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

Con Adaface, siamo stati in grado di ottimizzare il nostro processo di screening iniziale di oltre il 75%, liberando un tempo prezioso sia per i responsabili delle assunzioni che per il nostro team di acquisizione di talenti!

Brandon Lee, Capo delle persone, 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 Developer
Machine Learning Engineer
Data Scientist
Data Analyst
Artificial Intelligence Engineer
Data Engineer
Business Analyst
Research Scientist
Statistical Analyst
Data Mining Specialist

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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I responsabili delle assunzioni hanno ritenuto che attraverso le domande tecniche che hanno posto durante le interviste del panel, sono stati in grado di dire quali candidati avevano punteggi migliori e si sono differenziati con coloro che non hanno segnato. Sono molto soddisfatto Con la qualità dei candidati selezionati con lo screening di Adaface.

85%

Riduzione del tempo di screening

Visualizza il caso di studio completo

Machine Learning Online Test FAQs

Che tipo di domande include il test di apprendimento automatico?

Questo test di apprendimento automatico pre-assunzione è composto da domande basate su scenari che richiedono ai candidati di dimostrare la loro capacità di:

Preparare i dati per gli algoritmi di apprendimento automatico
Utilizzare algoritmi ML come regressione logistica, macchine vettoriali di supporto, alberi decisionali e foreste casuali per la classificazione
Costruisci algoritmi di clustering
Proponi l'algoritmo più appropriato per un caso d'uso specifico
stimare le prestazioni degli algoritmi di apprendimento

Questo test o valutazione può essere utilizzato per i ruoli di ingegnere di apprendimento automatico senior?

Per gli ingegneri di apprendimento automatico senior, è possibile richiedere un test personalizzato. Entro 48 ore i nostri esperti in materia personalizzeranno la valutazione in conformità con la descrizione del lavoro e il livello di anzianità. Un test tipico per ruoli senior, oltre ai fondamentali, il test si concentrerà sul test della capacità di un candidato di:

Struttura progetti ML
Identifica le carenze di vari algoritmi di apprendimento automatico
Progettare un processo di pulizia dei dati e etichettatura dei dati
Seleziona Metriche di valutazione adeguate per migliorare le prestazioni del modello
Valuta l'impatto delle prestazioni hardware sugli algoritmi di apprendimento automatico

Posso combinare più competenze in una valutazione personalizzata?

Si assolutamente. Le valutazioni personalizzate sono impostate in base alla descrizione del tuo lavoro e includeranno domande su tutte le competenze indispensabili che specificate.

Hai in atto delle caratteristiche anti-cheat o procuratore?

Abbiamo in atto le seguenti caratteristiche anti-cheat:

Domande non googiche
Proctoring IP
procuratore web
Proctor di webcam
Rilevamento del plagio
Sicuro browser

Leggi di più sulle caratteristiche di procuratore.

Come interpreto i punteggi dei test?

La cosa principale da tenere a mente è che una valutazione è uno strumento di eliminazione, non uno strumento di selezione. Una valutazione delle competenze è ottimizzata per aiutarti a eliminare i candidati che non sono tecnicamente qualificati per il ruolo, non è ottimizzato per aiutarti a trovare il miglior candidato per il ruolo. Quindi il modo ideale per utilizzare una valutazione è decidere un punteggio di soglia (in genere il 55%, ti aiutiamo a benchmark) e invitiamo tutti i candidati che segnano al di sopra della soglia per i prossimi round di intervista.

Per quale livello di esperienza posso usare questo test?

Ogni valutazione di Adaface è personalizzata per la descrizione del tuo lavoro/ personaggio del candidato ideale (i nostri esperti in materia sceglieranno le domande giuste per la tua valutazione dalla nostra biblioteca di oltre 10000 domande). Questa valutazione può essere personalizzata per qualsiasi livello di esperienza.

Ogni candidato riceve le stesse domande?

Sì, ti rende molto più facile confrontare i candidati. Le opzioni per le domande MCQ e l'ordine delle domande sono randomizzate. Abbiamo anti-cheatri/procuratore in atto. Nel nostro piano aziendale, abbiamo anche la possibilità di creare più versioni della stessa valutazione con questioni di difficoltà simili.

Sono un candidato. Posso provare un test di pratica?

No. Sfortunatamente, al momento non supportiamo i test di pratica. Tuttavia, è possibile utilizzare le nostre domande di esempio per la pratica.

Qual è il costo dell'utilizzo di questo test?

Puoi controllare i nostri piani di prezzo.

Posso avere una prova gratuita?

Sì, puoi iscriverti gratuitamente e visualizzare in anteprima questo test.

Sono appena passato a un piano a pagamento. Come posso richiedere una valutazione personalizzata?

Ecco una rapida guida su come richiedere una valutazione personalizzata su Adaface.

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Join 1200+ companies in 75+ countries.

Prova oggi lo strumento di valutazione delle competenze più candidati.

Inizia gratuitamente

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Machine Learning Assessment Test

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

Tests for on-the-job skills

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

No trick questions

Non-googleable questions

1200+ customers in 75 countries

Designed for elimination, not selection

1 click candidate invites

Detailed scorecards & benchmarks

High completion rate

Advanced Proctoring

About Machine Learning Developer Roles

Typical Machine Learning Developer/Engineer responsibilities include:

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

What topics are covered in the Machine Learning Assessment Test?

Machine Learning Online Test FAQs

Che tipo di domande include il test di apprendimento automatico?

Questo test o valutazione può essere utilizzato per i ruoli di ingegnere di apprendimento automatico senior?

Posso combinare più competenze in una valutazione personalizzata?

Hai in atto delle caratteristiche anti-cheat o procuratore?

Come interpreto i punteggi dei test?

Per quale livello di esperienza posso usare questo test?

Ogni candidato riceve le stesse domande?

Sono un candidato. Posso provare un test di pratica?

Qual è il costo dell'utilizzo di questo test?

Posso avere una prova gratuita?

Sono appena passato a un piano a pagamento. Come posso richiedere una valutazione personalizzata?

40%