AI Research Engineer Test

The AI Research Engineer Test evaluates a candidate's proficiency in machine learning, neural networks, language modeling, and statistics through scenario-based MCQs. It also includes coding questions to determine practical Python programming skills essential for AI research and development roles.

Covered skills:

Machine Learning
Neural Networks
Language Modeling
Statistics
Python Programming
Deep Learning
Supervised Learning
Unsupervised Learning
Natural Language Processing
Data Preprocessing
Model Evaluation
Optimization Techniques

Test Duration

~ 40 mins

Difficulty Level

Moderate

Questions

4 Machine Learning MCQs
4 Neural Networks MCQs
4 Language Modeling (LLMs) MCQs
4 Statistics MCQs
1 Python Coding Question

Availability

Available as custom test

Get started for free

Preview questions

About the AI Research Engineer Assessment Test

The AI Research Engineer 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:

Proficient in applying machine learning techniques to real-world problems.
Capable of designing and implementing neural networks for complex datasets.
Skilled in employing language models for natural language processing tasks.
Able to interpret and apply statistical methods to data analysis.
Competent in writing effective and efficient Python code for data science applications.
Experienced in training and optimizing deep learning models.
Knowledgeable in supervised learning algorithms and their applications.
Adept at using unsupervised learning techniques for data clustering and reduction.
Proficient in natural language processing using modern toolkits.
Experienced in data preprocessing for machine learning workflows.
Skilled in evaluating model performance using various metrics.
Capable of implementing optimization techniques for improving model accuracy.

1200+ customers in 80 countries

Use Adaface tests trusted by recruitment teams globally. Adaface skill assessments measure on-the-job skills of candidates, providing employers with an accurate tool for screening potential hires.

Get started for free

Preview questions

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 15,000+ questions. The actual questions on this AI Research Engineer Test will be non-googleable.

🧐 Question
Easy Gradient descent optimization Gradient Descent Learning Rate Schedules Optimization Techniques	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 Data Transformation Overfitting Prevention	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 String Manipulation Algorithm	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 Sensitivity Specificity	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?
Easy Frequency distribution	Solve
Convert the following into an ordinary frequency distribution: - 5 users gave less than 3 rating - 12 users gave less than 6 rating - 25 users gave less than 9 ratings - 33 users get less than 12 ratings

	🧐 Question	🔧 Skill
	Easy Gradient descent optimization Gradient Descent Learning Rate Schedules Optimization Techniques	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 Data Transformation Overfitting Prevention	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 String Manipulation Algorithm	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 Sensitivity Specificity	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?
	Easy Frequency distribution	3 mins Statistics	Solve
Convert the following into an ordinary frequency distribution: - 5 users gave less than 3 rating - 12 users gave less than 6 rating - 25 users gave less than 9 ratings - 33 users get less than 12 ratings

	🧐 Question	🔧 Skill	💪 Difficulty	⌛ Time
	Gradient descent optimization Gradient Descent Learning Rate Schedules Optimization Techniques	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 Data Transformation Overfitting Prevention	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 String Manipulation Algorithm	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 Sensitivity Specificity	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?
	Frequency distribution	Statistics	Easy	3 mins	Solve
Convert the following into an ordinary frequency distribution: - 5 users gave less than 3 rating - 12 users gave less than 6 rating - 25 users gave less than 9 ratings - 33 users get less than 12 ratings

Get started for free

Preview questions

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

It's very easy to share assessments with candidates and for candidates to use. We get good feedback from candidates about completing the tests. Adaface are very responsive and friendly to deal with.

Kirsty Wood, Human Resources, WillyWeather

We were able to close 106 positions in a record time of 45 days! Adaface enables us to conduct aptitude and psychometric assessments seamlessly. My hiring managers have never been happier with the quality of candidates shortlisted.

Amit Kataria, CHRO, Hanu

We evaluated several of their competitors and found Adaface to be the most compelling. Great library of questions that are designed to test for fit rather than memorization of algorithms.

Swayam Narain, CTO, Affable

Why you should use Pre-employment AI Research Engineer Test?

The AI Research Engineer 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:

Understanding basic machine learning models
Implementing simple neural networks
Comprehending language modeling techniques
Applying statistical methods for data analysis
Writing efficient Python code
Training supervised learning models
Understanding unsupervised learning concepts
Performing data preprocessing techniques
Evaluating machine learning model performance
Utilizing optimization techniques in machine learning

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 AI Research Engineer Test?

Machine Learning: Machine Learning involves creating systems that learn and improve from experience without explicit programming. It's crucial for developing models that can make predictions or decisions based on data, a core aspect of AI research engineering.

Neural Networks: Neural Networks are computational models inspired by the human brain, consisting of interconnected nodes, or neurons. They are fundamental in understanding and building deep learning systems due to their ability to recognize complex patterns in data.

Language Modeling: Language Modeling focuses on predicting the next word in a sentence, a foundational task in natural language processing. Measuring this skill ensures proficiency in building models that can understand and generate human language efficiently.

Statistics: Statistics involves the collection, analysis, interpretation, and presentation of data, critical for validating model assumptions and results. It provides the mathematical foundation necessary for any data-driven decision-making process.

Python Programming: Python Programming is essential due to its simplicity and extensive library support for AI development. Proficiency in Python allows researchers to implement various algorithms efficiently and effectively, promoting rapid prototyping and deployment.

Deep Learning: Deep Learning is a subset of machine learning involving neural networks with many layers, enabling the modeling of complex data representations. Understanding deep learning is vital for pioneering advancements in AI research.

Supervised Learning: Supervised Learning is a type of machine learning where the model is trained on a labeled dataset. This skill ensures one can develop models that can generalize well to unseen data, making accurate predictions.

Unsupervised Learning: Unsupervised Learning deals with finding hidden patterns in unlabeled data. It's important for exploratory data analysis and scenarios where predefined labels are unavailable, expanding an AI system's robustness.

Natural Language Processing: Natural Language Processing encompasses a range of techniques for analyzing and generating human language. This skill allows machines to interpret text, speech, and intent, crucial for interactive AI systems.

Data Preprocessing: Data Preprocessing involves cleaning and transforming raw data into a suitable format for analysis. This step is critical to ensure data quality and to prepare datasets effectively for building reliable AI models.

Model Evaluation: Model Evaluation is the process of assessing a model's performance on unseen data. It ensures that the AI systems being developed are accurate, robust, and able to generalize well beyond the training data.

Optimization Techniques: Optimization Techniques are mathematical methods used to adjust model parameters to minimize error and improve performance. Mastering these techniques is essential for fine-tuning AI models to achieve their best possible performance.

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 AI Research Engineer Test to be based on.

Regression Analysis

Classification Algorithms

Decision Trees

Support Vector Machines

K-Means Clustering

Neural Network Basics

Convolutional Networks

Recurrent Networks

Transformers

BERT Model

GPT Models

Tokenization

Word Embeddings

Data Normalization

Data Augmentation

Hyperparameter Tuning

Gradient Descent

Loss Functions

Cross-Validation

A/B Testing

Probability Distributions

Hypothesis Testing

Linear Regression

Logistic Regression

Python Pandas

Python NumPy

Python Matplotlib

Python Scikit-learn

Artificial Intelligence

Deep Learning

Model Overfitting

Model Underfitting

Feature Engineering

Dimensionality Reduction

Principal Component Analysis

Autoencoders

Reinforcement Learning

Single Layer Perceptron

Multi Layer Perceptron

Bayesian Statistics

Time Series Analysis

Ensemble Methods

Random Forest

Gradient Boosting

XGBoost

Python Syntax

Exception Handling

Data Visualization

SQL Queries

Big Data Analytics

What roles can I use the AI Research Engineer Test for?

AI Research Engineer
Machine Learning Engineer
Data Scientist
Deep Learning Engineer
NLP Engineer
Data Analyst
Artificial Intelligence Specialist
Research Scientist
Software Engineer
Python Developer

How is the AI Research Engineer 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

Designing complex neural network architectures
Developing advanced natural language processing models
Applying deep learning frameworks effectively
Implementing advanced model evaluation metrics
Optimizing hyperparameters for better model performance
Integrating machine learning pipelines
Utilizing advanced statistical analysis methods
Deploying machine learning models to production
Developing custom loss functions in neural networks
Implementing advanced data augmentation techniques

The coding question for experienced candidates will be of a higher difficulty level to evaluate more hands-on experience.

Preview this test

View sample scorecard

Try the most advanced candidate assessment platform

AI Cheating Detection with Honestly

ChatGPT Protection

Non-googleable Questions

Web Proctoring

IP Proctoring

Webcam Proctoring

MCQ Questions

Coding Questions

Typing Questions

Personality Questions

Custom Questions

Ready-to-use Tests

Custom Tests

Custom Branding

Bulk Invites

Public Links

ATS Integrations

Multiple Question Sets

Custom API integrations

Role-based Access

Priority Support

GDPR Compliance

Screen candidates in 3 easy steps

Pick a test from over 500+ tests

The Adaface test library features 500+ tests to enable you to test candidates on all popular skills- everything from programming languages, software frameworks, devops, logical reasoning, abstract reasoning, critical thinking, fluid intelligence, content marketing, talent acquisition, customer service, accounting, product management, sales and more.

Invite your candidates with 2-clicks

Make informed hiring decisions

Get started for free

Preview questions

Have questions about the AI Research Engineer Hiring Test?

What is AI Research Engineer Test?

The AI Research Engineer Test is designed to evaluate candidates' expertise in AI concepts and programming. Recruiters use it to assess skills like Machine Learning, Neural Networks, and Python Programming. It's helpful for identifying skilled candidates for research positions.

Can I combine AI Research Engineer Test with Deep Learning questions?

Yes, recruiters can request a custom test with multiple skills, including Deep Learning. Check out our Deep Learning Test for more details on how we assess this skill.

What kind of questions are used to evaluate senior candidates in the AI Research Engineer Test?

The test assesses senior candidates on advanced skills like designing complex neural network architectures, developing NLP models, optimizing hyperparameters, and deploying ML models to production.

How to use AI Research Engineer Test in my hiring process?

Use the test in the pre-screening phase. Add a link in your job post or invite candidates by email. It helps in identifying skilled candidates early.

What are the main AI and Machine Learning tests?

Explore more in our AI and Machine Learning category:

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:

Hidden AI Tools Detection with Honestly
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.

View sample scorecard

Along with scorecards that report the performance of the candidate in detail, you also receive a comparative analysis against the company average and industry standards.

View sample scorecard