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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, 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
  • Gradient Descent
  • Accuracy Matrix
See all covered skills

9 reasons why
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

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.

Dies sind nur ein kleines Beispiel aus unserer Bibliothek mit mehr als 10.000 Fragen. Die tatsächlichen Fragen dazu Machine Learning Assessment Test wird nichtgänger sein.

🧐 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

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?
 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?
🧐 Question🔧 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

Medium

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
🧐 Question🔧 Skill💪 Difficulty⌛ 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
Medium2 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

Mit Adaface konnten wir unseren ersten Screening -Prozess um über 75%optimieren und die wertvolle Zeit sowohl für Einstellungsmanager als auch für unser Talentakquisitionsteam gleichermaßen freien!


Brandon Lee, Leiter der Menschen, 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.

invite candidates
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.

test completion rate
Reason #9

Advanced Proctoring


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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Die Einstellungsmanager waren der Ansicht, dass sie durch die technischen Fragen, die sie während der Panel -Interviews gestellt hatten, erkennen konnten, welche Kandidaten bessere Punktzahlen hatten, und sich von denen unterschieden, die nicht so gut erzielten. Sie sind Sehr zufrieden Mit der Qualität der Kandidaten, die mit dem Adaface -Screening in die engere Wahl gezogen wurden.


85%
Verringerung der Screening -Zeit

Machine Learning Online Test FAQs

Welche Art von Fragen beinhaltet der maschinelle Lerntest?

Dieser maschinelle Lernentest vor der Beschäftigung besteht aus szenariobasierten Fragen, bei denen Kandidaten ihre Fähigkeit demonstrieren müssen:

  • Bereiten Sie Daten für Algorithmen für maschinelles Lernen vor
  • Verwenden Sie ML -Algorithmen wie logistische Regression, Unterstützungsvektormaschinen, Entscheidungsbäume und zufällige Wälder zur Klassifizierung
  • Baualgorithmen erstellen
  • Schlagen Sie den am besten geeigneten Algorithmus für einen bestimmten Anwendungsfall vor
  • Schätzen Sie die Leistung von Lernalgorithmen

Kann dieser Test oder diese Bewertung für Rollen des maschinellen Lernens für maschinelles Lernen verwendet werden?

Für Senior Learning Engineers können Sie einen benutzerdefinierten Test anfordern. Innerhalb von 48 Stunden passen unsere Fach -Experten die Bewertung gemäß Ihrer Stellenbeschreibung und Seniorität an. Ein typischer Test für Senior -Rollen, zusätzlich zu den Grundlagen wird sich der Test darauf konzentrieren, die Fähigkeit eines Kandidaten zu testen:

  • Struktur -ML -Projekte
  • Ermitteln Sie Mängel verschiedener Algorithmen für maschinelles Lernen
  • Entwerfen Sie einen Datenreinigungs- und Datenkennzeichnungsprozess
  • Wählen Sie die richtigen Bewertungsmetriken aus, um die Modellleistung zu verbessern
  • Bewerten Sie die Auswirkungen der Hardwareleistung auf die Algorithmen für maschinelles Lernen

Kann ich mehrere Fähigkeiten zu einer benutzerdefinierten Bewertung kombinieren?

Ja absolut. Basierend auf Ihrer Stellenbeschreibung werden benutzerdefinierte Bewertungen eingerichtet und enthalten Fragen zu allen von Ihnen angegebenen Must-Have-Fähigkeiten.

Haben Sie Anti-Cheating- oder Proctoring-Funktionen?

Wir haben die folgenden Anti-Cheating-Funktionen:

  • Nicht-Googling-Fragen
  • IP -Verbreitung
  • Web -Verbreitung
  • Webcam -Proctoring
  • Plagiaterkennung
  • sicherer Browser

Lesen Sie mehr über die Proctoring -Funktionen.

Wie interpretiere ich die Testergebnisse?

Die wichtigste Sache, die Sie beachten sollten, ist, dass eine Bewertung ein Eliminierungswerkzeug ist, kein Auswahlwerkzeug. Eine Bewertung der Qualifikationsbewertung wird optimiert, um Ihnen zu helfen, Kandidaten zu beseitigen, die technisch nicht für die Rolle qualifiziert sind. Sie ist nicht optimiert, um Ihnen dabei zu helfen, den besten Kandidaten für die Rolle zu finden. Die ideale Möglichkeit, eine Bewertung zu verwenden, besteht also darin, einen Schwellenwert zu entscheiden (in der Regel 55%, wir helfen Ihnen bei der Benchmark) und alle Kandidaten einladen, die für die nächsten Interviewrunden über dem Schwellenwert punkten.

Für welche Erfahrung kann ich diesen Test verwenden?

Jede Adaface -Bewertung ist an Ihre Stellenbeschreibung/ ideale Kandidatenpersönlichkeit angepasst (unsere Experten für Fache werden die richtigen Fragen für Ihre Bewertung aus unserer Bibliothek mit über 10000 Fragen auswählen). Diese Einschätzung kann für jede Erfahrungsstufe angepasst werden.

Bekommt jeder Kandidat die gleichen Fragen?

Ja, es macht es Ihnen viel einfacher, Kandidaten zu vergleichen. Optionen für MCQ -Fragen und die Reihenfolge der Fragen werden randomisiert. Wir haben Anti-Cheating/Proctoring Funktionen. In unserem Unternehmensplan haben wir auch die Möglichkeit, mehrere Versionen derselben Bewertung mit Fragen mit ähnlichen Schwierigkeitsgraden zu erstellen.

Ich bin ein Kandidat. Kann ich einen Übungstest ausprobieren?

Nein, leider unterstützen wir derzeit keine Übungstests. Sie können jedoch unsere Beispielfragen zur Praxis verwenden.

Was kostet die Verwendung dieses Tests?

Sie können unsere Preispläne überprüfen.

Kann ich eine kostenlose Testversion erhalten?

Ja, Sie können sich kostenlos anmelden und eine Vorschau dieses Tests.

Ich bin gerade zu einem bezahlten Plan gezogen. Wie kann ich eine benutzerdefinierte Bewertung anfordern?

Hier finden Sie eine kurze Anleitung zu wie Sie eine benutzerdefinierte Bewertung anfordern auf Adaface.

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40 min tests.
No trick questions.
Accurate shortlisting.
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