AI and machine learning assessment test for hiring
Compare 14 AI and machine learning tests by skill and level. Choose assessments for model building, language, vision, reinforcement learning, or cloud ML work.
The short answer
These 14 multiple-choice tests take 10 minutes each and cover AI methods, classical and deep learning, neural networks, language and vision models, reinforcement learning, and cloud tools. Intermediate options suit a range of AI and machine learning roles; advanced tests focus on computer vision and multi-task learning. Use results as evidence for review.
14 tests
Artificial Intelligence
10 minutes · 15 questions · Intermediate
Multiple choice
See the testProgramming skillsComputer Vision
10 minutes · 15 questions · Advanced
Multiple choice
See the testRole-specific skillsDeep Learning
10 minutes · 15 questions · Intermediate
Multiple choice
See the testRole-specific skillsMachine Learning
10 minutes · 15 questions · Intermediate
Multiple choice
See the testProgramming skillsMachine Learning in Amazon Web Services (AWS)
10 minutes · 15 questions · Intermediate
Multiple choice
See the testProgramming skillsMachine Learning in Azure
10 minutes · 15 questions · Intermediate
Multiple choice
See the testProgramming skillsMachine Learning in Google Cloud Platform (GCP)
10 minutes · 15 questions · Intermediate
Multiple choice
See the testProgramming skillsMulti-Task Learning (MTL)
10 minutes · 15 questions · Advanced
Multiple choice
See the testRole-specific skillsNatural Language Processing (NLP)
10 minutes · 15 questions · Intermediate
Multiple choice
See the testProgramming skillsNeural Networks
10 minutes · 15 questions · Intermediate
Multiple choice
See the testProgramming skillsPyTorch
10 minutes · 15 questions · Intermediate
Multiple choice
See the testRole-specific skillsReinforcement Learning (RL)
10 minutes · 15 questions · Intermediate
Multiple choice
See the testProgramming skillsScikit-learn
10 minutes · 15 questions · Intermediate
Multiple choice
See the testProgramming skillsTensorFlow
10 minutes · 15 questions · Intermediate
Multiple choice
See the testTests by category
- Programming skills (9 tests)
- Role-specific skills (5 tests)
What the AI and machine learning tests cover
The tests cover both general methods and focused technical work. The Artificial Intelligence test measures applying search, uncertainty, games and constraints, logic, and planning to small problems. The Machine Learning test focuses on choosing, tuning, and reading results from classical methods. For neural models, Deep Learning covers math, architecture, training, regularization, and neural networks for images.
Other tests focus on specific data or model needs. Natural Language Processing (NLP) covers text preparation, text models, information extraction, and transformer language models. Computer Vision tests judgment about image operations, detection, pixel-level labeling, camera geometry, and deployment. Reinforcement Learning (RL) covers foundations, model-free methods, deep value and policy methods, and agent training.
The library also includes framework tests such as PyTorch, Scikit-learn, and TensorFlow, plus tests for cloud ML and multi-task learning. All tests in this group are multiple choice and take 10 minutes.
Choose tests for the role and level
For data scientists, data analysts, data engineers, and AI or machine learning engineers who need intermediate knowledge, consider Machine Learning or Artificial Intelligence. The Machine Learning test also suits technical leads and product people whose work calls for intermediate ML knowledge. For engineers and analysts working with text, Natural Language Processing (NLP) measures practical NLP knowledge.
For model builders working at an intermediate level, Neural Networks covers designing, training, debugging, and applying networks. Choose a framework-specific test when the role calls for it: PyTorch suits people building or maintaining PyTorch models, while TensorFlow focuses on TensorFlow models, data pipelines, and training. The Scikit-learn test suits people who build or review models with that library.
The advanced Computer Vision test is aimed at engineers working on image or video models, perception, or visual data. Multi-Task Learning (MTL) is advanced and suits senior engineers, researchers, and scientists building models for multiple prediction tasks. These tests measure different skills, so select those tied to the role's actual work.
Combine assessments around the work
Start with one test for the role's central skill, then add a focused test only when the work calls for that additional evidence. For an intermediate machine learning engineer, pair Machine Learning with Deep Learning if deep learning is also needed. For a senior role that builds or deploys models on images or video, add the advanced Computer Vision test.
For a text-focused role, Natural Language Processing (NLP) can be paired with Machine Learning when both areas matter. For a role that builds learning agents, consider Reinforcement Learning (RL) alongside Artificial Intelligence to assess agent methods as well as core AI approaches.
A cloud test can add evidence about deployment and operations rather than model theory. Choose Machine Learning in Azure, Machine Learning in Amazon Web Services (AWS), or Machine Learning in Google Cloud Platform (GCP) based on the platform named for the role. Review each score as evidence alongside other information about the candidate, not as a decision by itself.
How these tests keep questions fair
Each test takes practical steps to keep questions focused on the skill being assessed. For Computer Vision, images are described through arrays, tables, shapes, and short code rather than photographs. Questions do not depend on recognizing people or places, or on distinguishing colors by sight. Deep Learning uses short, framework-neutral Python, and states library conventions where needed.
Other tests avoid dependence on a particular tool's defaults. Machine Learning asks about concepts rather than the function names or defaults of one library. Neural Networks states the shapes, settings, and conventions needed to answer, rather than requiring knowledge of one framework's names or defaults. The PyTorch test uses short, self-contained code and does not require a GPU.
Across the group, wording is plain, scenarios are neutral, and calculations are small enough to work by hand. Reinforcement Learning (RL) defines every symbol where it is used, and Natural Language Processing (NLP) uses only built-in Python and its standard library, never a third-party library. Several tests, including TensorFlow and Artificial Intelligence, allow scratch paper for every candidate. A score is one source of evidence for your review, not a verdict about a candidate.
Frequently asked questions
Which tests suit a role that builds learning agents or game AI?+
Reinforcement Learning (RL) suits engineers and game AI developers who build or tune learning agents. It covers model-free methods, deep value and policy methods, and practical ways to make agent training work. Artificial Intelligence also names game developers who build AI behavior, and covers search, games and planning.
Which test fits machine learning work on a cloud platform?+
Choose the test for the platform the role uses: Machine Learning in Azure, Machine Learning in Amazon Web Services (AWS), or Machine Learning in Google Cloud Platform (GCP). Each is intermediate and suits machine learning engineers and data scientists. Questions test working judgment rather than memory of prices or quotas, which change often.
Which AI and machine learning tests are advanced?+
Computer Vision and Multi-Task Learning (MTL) are advanced. Computer Vision assesses practical judgment about visual data and deployment. Multi-Task Learning assesses designing, training, adapting, and evaluating models that share learning across several tasks.
How should I combine AI and machine learning tests?+
Choose a test for the role's main skill, then add a focused assessment if the work requires another area. For example, pair Machine Learning with Natural Language Processing (NLP) for a role that works with both general ML methods and text.
Are AI and machine learning tests tied to one framework?+
Some tests are framework-specific, including PyTorch, TensorFlow, and Scikit-learn. Others assess broader concepts or methods. For example, the Machine Learning test focuses on methods rather than the defaults or function names of a particular library.
How should I use AI and machine learning test scores?+
Treat a score as evidence to review with other information about the candidate. These tests measure specific skills, but a score alone should not decide whether someone is right for a role.
Build an assessment from these tests
Pick the tests for your role, invite candidates and compare their results.