**Neural Networks Basics**: Neural Networks Basics refers to the fundamental concepts and principles of neural networks, including the structure, operation, and learning algorithms. This skill is measured in the test to evaluate the candidate's understanding of the foundation of neural networks and their ability to apply this knowledge in practical scenarios.

**Shallow Neural Networks**: Shallow Neural Networks focus on neural networks with only one hidden layer. This skill assesses the candidate's understanding of designing and training simple neural networks for relatively straightforward tasks.

**Deep Neural Networks**: Deep Neural Networks involve neural networks with multiple hidden layers. This skill evaluates the candidate's expertise in developing and optimizing complex neural networks to tackle more intricate problems that require hierarchical representation learning.

**Deep Learning**: Deep Learning encompasses the broader field of using deep neural networks to learn and extract meaningful patterns from large, unstructured datasets. Measuring this skill assesses the candidate's ability to leverage deep learning techniques effectively and utilize state-of-the-art architectures and algorithms for real-world applications.

**Machine Learning**: Machine Learning focuses on training algorithms and statistical models that enable computers to learn from and make predictions or decisions based on data. Measuring this skill helps evaluate the candidate's grasp of machine learning concepts, including feature engineering, model selection, and performance evaluation.

**Python**: Python is a widely used programming language in data science and machine learning. This skill assesses the candidate's ability to write Python code to implement neural networks and apply various data manipulation and analysis techniques using libraries such as NumPy and Pandas.

**Data Science**: Data Science encompasses the interdisciplinary field of extracting insights and knowledge from data through various scientific methods, algorithms, and processes. Measuring this skill evaluates the candidate's understanding of data pre-processing, visualization, feature extraction, and other essential aspects required for solving real-world problems.

**NumPy**: NumPy is a fundamental library in Python for numerical computing and efficient handling of large multi-dimensional arrays and matrices. This skill measures the candidate's proficiency in utilizing NumPy for mathematical operations, linear algebra, and data manipulation tasks, which are crucial in building and training neural networks.