Machine Learning Concepts

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About the test

The Machine Learning Concepts assessment is designed to evaluate the knowledge and skills of entry-level to mid-level information technology professionals in designing and developing machine learning models for common business applications. The test measures a broad range of competencies including supervised and unsupervised learning algorithms, model evaluation and metrics, overfitting and underfitting, data preprocessing and feature engineering, and training, validation, and test set splits. It assesses familiarity with key algorithms such as linear regression, logistic regression, decision trees, random forests, and k-means clustering, as well as tools and frameworks including scikit-learn, TensorFlow, and PyTorch.

The assessment uses a variety of question formats including multiple choice, multiple correct answer, and essay composition questions, with questions drawn from a large pool to help prevent cheating. One or more questions are scored using artificial intelligence, and certain questions include individual time limits to further deter cheating and enhance predictive performance.

The specific attributes measured include:

Knowledge and Skills

Data Preprocessing and Feature Engineering
Model Evaluation and Metrics
Overfitting, Underfitting, and Model Generalization
Supervised Learning Algorithms
Training, Validation, and Test Set Splits
Unsupervised Learning and Clustering

Following completion, the test produces a Candidate Selection Report that includes an overall score and detailed scores for each attribute.

The Candidate Selection Report also provides expert interview questions that help you probe critical or low-scoring areas, along with a guide for noting your evaluation of the candidate's responses. Consistent use of an interview guide is an important part of gaining better candidate insights and making better hiring decisions.

Note that while the Machine Learning Concepts assessment is a useful and efficient instrument for confirming a base level of skill or knowledge about this important topic, it is not intended to pinpoint the exact level of candidate or skill. We recommend that skills tests, like this one, be used in parallel with other measurements, such as cognitive ability, job fit, and behavioral history. Each different type of test provides a valuable piece of information you can use when evaluating potential job performance.