ML Research Scientist Test

The ML Research Scientist Test assesses a candidate's knowledge in machine learning, neural networks, language modeling, and statistics. It includes scenario-based MCQs to evaluate conceptual understanding and a coding question to test Python programming skills relevant to ML research.

Covered skills:

Machine Learning Basics
Neural Networks
Language Modeling
Statistics for Data Science
Python Programming
Supervised Learning Techniques
Unsupervised Learning Algorithms
Deep Learning Frameworks
Natural Language Processing
Data Preprocessing
Model Evaluation
Feature Engineering

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

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About the ML Research Scientist Assessment Test

The ML Research Scientist 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:

Demonstrate understanding of fundamental machine learning concepts and algorithms.
Apply neural network architectures and comprehend their operations and optimization techniques.
Analyze and implement language modeling approaches for natural language processing tasks.
Utilize statistical methods to extract insights from data and validate predictive models.
Write efficient and effective Python code for data analysis and model implementation.
Distinguish between supervised and unsupervised learning techniques and apply them appropriately.
Implement and manipulate deep learning frameworks for building complex models.
Preprocess and clean data to enhance the accuracy and efficiency of model training.
Evaluate and interpret model performance using relevant metrics and validation techniques.
Perform feature engineering to enhance model predictiveness and reduce dimensionality.

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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 ML Research Scientist 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

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Why you should use Pre-employment ML Research Scientist Test?

The ML Research Scientist 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 fundamental machine learning concepts.
Implementing basic neural network architectures.
Performing language modeling tasks.
Applying statistical methods for data analysis.
Writing efficient Python code.
Classification and regression using supervised learning.
Clustering and dimensionality reduction techniques.
Deploying simple deep learning models.
Basic natural language processing tasks.
Cleaning and preprocessing datasets.

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 ML Research Scientist Test?

Machine Learning Basics: Understanding the fundamentals of machine learning is crucial as it serves as the foundation for more advanced topics in the field. This includes concepts like regression, classification, and clustering, all of which are essential for building intelligent systems. Without a firm grasp on these basics, tackling complex machine learning problems becomes significantly more challenging.

Neural Networks: Neural networks are at the core of modern artificial intelligence, capable of handling a variety of tasks by simulating the way a human brain operates. They enable the development of models that can recognize patterns and make informed predictions. Mastery of neural networks is necessary for cutting-edge research and innovation.

Language Modeling: Language modeling is a key aspect of natural language processing, enabling machines to understand and generate human language. By training on vast amounts of text, language models can predict word sequences and identify semantic relationships. This skill is critical for creating applications like chatbots and translators.

Statistics for Data Science: Statistics is the backbone of data science, providing tools to analyze data and infer meaningful patterns. It helps in understanding data distributions, testing hypotheses, and making decisions based on data. Proficiency in statistics ensures that researchers can appropriately interpret results and derive valid conclusions.

Python Programming: Python is the quintessential language for machine learning, bolstered by robust libraries and an active community. Its simplicity and readability make it perfect for implementing complex algorithms with ease. Proficiency in Python is essential for efficient model development and experimentation.

Supervised Learning Techniques: Supervised learning involves training models with labeled data to make predictions or classify incoming information. It's widely used in predictive analytics and helps optimize systems in various domains. Understanding these techniques is crucial for developing reliable and accurate models.

Unsupervised Learning Algorithms: Unlike supervised learning, unsupervised learning deals with unlabeled data, finding hidden patterns without specific guidance. It includes methods like clustering and dimensionality reduction, which are vital for data exploration. Knowledge of these algorithms is indispensable for uncovering insights in raw data.

Deep Learning Frameworks: Frameworks such as TensorFlow and PyTorch facilitate the construction and training of deep learning models. They provide streamlined interfaces and utilities for handling large datasets and complex computations. Familiarity with these frameworks accelerates the process of deploying sophisticated AI solutions.

Natural Language Processing: Natural Language Processing (NLP) enables machines to interpret and respond to human language in a valuable way. It involves tasks such as sentiment analysis, entity recognition, and machine translation. Proficiency in NLP can drive the development of intelligent systems capable of human-like understanding.

Data Preprocessing: Data preprocessing is the vital step of cleaning and transforming raw data into a suitable format for model training. Techniques include normalization, encoding, and handling missing values, crucial for improving model accuracy. Effective preprocessing ensures higher quality input data, leading to better model performance.

Model Evaluation: Evaluating a model's performance is key to understanding its effectiveness in making predictions. It involves metrics like accuracy, precision, recall, and F1-score, which help measure the model's success. Systematic evaluation is necessary to refine models and enhance predictive capabilities.

Feature Engineering: Feature engineering is the process of selecting and transforming variables to improve model outcomes. It involves creating meaningful features that can provide better insights and enhance predictive power. These skills are integral to boosting the overall performance of machine learning models.

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 ML Research Scientist Test to be based on.

Linear Regression

Logistic Regression

Decision Trees

Support Vector Machine

K-Means Clustering

Hierarchical Clustering

K-Nearest Neighbors

Random Forest

Gradient Boosting

Backpropagation

Convolutional Networks

Recurrent Networks

Transfer Learning

Word Embeddings

GPT Models

BERT Models

Statistical Hypothesis

Bayesian Statistics

Probability Distributions

Feature Scaling

Data Normalization

Data Encoding

Overfitting Handling

Cross-Validation

Confusion Matrix

ROC Curve

Precision Recall

Python Functions

Python Libraries

Error Handling

Data Visualization

Nested Lists

List Comprehension

Lambda Functions

Neural Activation

Learning Rate

Optimization Techniques

Train Test Split

Hyperparameter Tuning

Loss Functions

Regularization Methods

Data Augmentation

Batch Normalization

Dropout Regularization

Paragraph Vectors

Tokenization

N-gram Models

Parse Trees

Named Entity Recognition

Sentiment Analysis

Bag of Words

What roles can I use the ML Research Scientist Test for?

Machine Learning Research Scientist
Data Scientist
AI Engineer
Deep Learning Specialist
NLP Engineer
Computational Linguist
Data Analyst
Statistical Data Analyst
Research Scientist in AI
Python Developer

How is the ML Research Scientist 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

Conducting data-driven model evaluations.
Designing neural networks for complex tasks.
Optimizing models using advanced techniques.
Evaluating performance of language models.
Interpreting statistical results in research.
Developing scalable Python applications.
Experimenting with advanced supervised algorithms.
Adopting unsupervised learning in real-world scenarios.
Integrating deep learning frameworks into projects.
Engineering features for increased model accuracy.

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

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Have questions about the ML Research Scientist Hiring Test?

What is ML Research Scientist Test?

The ML Research Scientist Test evaluates a candidate's expertise in machine learning and related areas. It is used by recruiters to identify skilled professionals for roles involving complex data science tasks. This test is valuable for assessing knowledge in key topics pertinent to machine learning research.

Can I combine the ML Research Scientist Test with Deep Learning questions?

Yes, you can request a custom test that integrates Deep Learning questions. We offer a Deep Learning Online Test in our library to further assess this skill. It complements the ML Research Scientist Test well.

What topics are evaluated in the ML Research Scientist Test?

The test assesses topics including Machine Learning Basics, Neural Networks, Language Modeling, Statistics for Data Science, and Python Programming. Advanced concepts like supervised and unsupervised learning, deep learning frameworks, and model evaluation are also included.

How to use the ML Research Scientist Test in my hiring process?

Incorporate the assessment as a pre-screening tool at the recruitment's initial stage. Share a direct link to the test in job postings or invite candidates via email. This streamlines identifying skilled candidates effectively.

Can I test Python and ML together in a test?

Certainly! Our platform offers a Python & Machine Learning Assessment Test which is recommended for roles requiring both skill sets. This integrated approach provides a comprehensive evaluation.

What are the main Data Science tests?

For data science evaluations, we offer several key tests including the Data Science Assessment Test, Machine Learning Assessment Test, and Python Pandas Test. These tests cover various aspects of data science skill sets.

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.

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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.

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