The Sigmoid activation function is commonly used in the output layer of a binary classification neural network. It maps the network's output to a probability between 0 and 1, making it suitable for binary classification tasks. The other activation functions are more commonly used in hidden layers or for other types of problems.

One major drawback of using the sigmoid activation function in deep networks is its susceptibility to the vanishing gradient problem. This can hinder training deep networks as the gradient becomes very small for extreme values, slowing down learning.

When normalizing a database, creating a new primary and foreign key relationship by separating data into two tables is part of the Second Normal Form (2NF). 2NF eliminates partial dependencies and ensures that every non-key attribute is functionally dependent on the entire primary key. This is an essential step in achieving a fully normalized database.

In complex ETL (Extract, Transform, Load) processes, "Data lineage" is crucial for ensuring data quality and accuracy. Data lineage helps track the origin and transformation of data, ensuring that the data remains reliable and traceable throughout the pipeline.

AUC-ROC stands for Area Under the Receiver Operating Characteristic curve. The ROC curve is a graphical representation of a model's performance, particularly its ability to distinguish between the positive and negative classes. AUC (Area Under the Curve) quantifies the overall performance of the model, with higher AUC values indicating better discrimination.

Fine-tuning in transfer learning involves taking a pre-trained model's architecture and training it with new data, adjusting the model's parameters to suit the specific task. It's a common technique for leveraging pre-trained models for custom tasks.

In datasets with multiple features, a heatmap plot can be used to visualize the relationship between variables. It provides a color-coded matrix to represent the correlations between features, making it a useful tool for detecting multivariate outliers and understanding the relationships between variables.

Cassandra is a NoSQL database system based on the wide-column store model. It is designed for distributed data storage, making it suitable for handling large volumes of data across multiple nodes in a distributed environment. MySQL, PostgreSQL, and Oracle are relational database management systems, not wide-column stores.

Apache Spark uses RDD (Resilient Distributed Dataset) as its core data structure for distributed data processing. RDDs are immutable, fault-tolerant collections of data that can be processed in parallel.

Slowly Changing Dimensions (SCD) is a technique used in data warehousing to maintain the history of data changes. It allows the storage of historical data, which is essential for tracking changes and trends over time in a data warehouse.