The correct term is "Neural Network." Neural networks, specifically deep neural networks, are capable of modeling non-linear complex relationships in large datasets. These networks consist of multiple hidden layers that allow them to capture intricate patterns and relationships within data. They are especially effective in tasks such as image recognition, natural language processing, and complex data transformations.

Pooling (subsampling) is used in CNNs to reduce the spatial dimensions of the input, allowing the network to focus on the most relevant features. It helps control the computational complexity and overfitting.

Apache Spark achieves faster data processing by using in-memory processing. Unlike traditional MapReduce, which writes intermediate results to disk, Spark caches intermediate data in memory, reducing I/O operations and speeding up data processing significantly. This in-memory processing is one of Spark's key features for performance optimization.

Exploratory Data Analysis (EDA) begins with descriptive statistics to understand the basic characteristics of a dataset, such as mean, median, and standard deviation. These statistics provide an initial overview of the data before diving into more complex analyses.

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.

In a microservices architecture, Docker (Option A) is often used for containerization. Docker allows you to package the machine learning model and its dependencies into a container, making it easy to deploy and manage in various environments.