When dealing with skewed datasets, the median is preferred for imputation. The median is robust to extreme values and is less affected by outliers than the mean. Using the median as the measure of central tendency helps maintain the integrity of the dataset in the presence of outliers.

Data Engineers are responsible for the efficient collection, storage, and processing of data. They create the infrastructure necessary for Data Scientists and Analysts to work with data effectively.

Min-Max Scaling, also known as feature scaling, is used to transform values within a specific range (typically 0 to 1) for different features. It ensures that variables with different scales have a similar impact on the analysis.

Exploratory Data Analysis (EDA) often employs dimensionality reduction techniques like Principal Component Analysis (PCA) to visualize data in lower-dimensional spaces (2 or 3 dimensions) for better understanding, hence the term "dimensions."

The primary goal of tokenization in NLP is to split text into words or tokens. This process is essential for various NLP tasks such as text analysis, language modeling, and information retrieval. Tokenization helps in breaking down text into meaningful units for analysis.

Batch Normalization is a technique used to improve the training of deep neural networks. It addresses the internal covariate shift problem by normalizing the activations of each layer. This helps in accelerating training and allows for the use of higher learning rates without the risk of divergence. It also aids in better gradient flow.

MongoDB is a NoSQL database that is ideal for handling hierarchical data and provides better scalability for unstructured data. MongoDB uses BSON (Binary JSON) format, which makes it a good choice for applications that require flexibility and scalability in dealing with complex data structures.

This scenario highlights the ethical concern of "Data bias in AI." The AI model's biased selection towards male candidates indicates that the training data may be biased, leading to unfair and discriminatory outcomes. Addressing data bias is essential to ensure fairness and diversity in AI-driven recruitment.

Random Forest is a suitable choice for mixed input features when interpretability is important. It combines decision trees and is often used for feature selection and interpretability, making it a good option for such cases.

A Foreign Key is used in a relational database to ensure data integrity by creating a link between tables. It enforces referential integrity, ensuring that values in one table match values in another. Primary Keys are used to uniquely identify records in a table, not to maintain integrity across tables. Indexes and Triggers serve different purposes.