Latent Semantic Analysis (LSA) is an NLP technique that transforms text into a meaningful vector space by capturing latent semantic relationships between words. It helps in reducing the dimensionality of text data while preserving its meaning. The other options are not methods for transforming text into numerical vectors and serve different purposes in NLP and data analysis.

One of the most popular algorithms used in collaborative filtering for recommender systems is Singular Value Decomposition (SVD). SVD is a matrix factorization technique that can be used to make recommendations based on user-item interactions.

Matrix factorization techniques primarily address the sparsity problem in recommender systems. In such systems, user-item interaction data is typically sparse, and matrix factorization helps in predicting missing values by factoring the observed data matrix into latent factors. This mitigates the sparsity challenge.

In Transformer architectures, the mechanism that allows the model to focus on different parts of the input data differently is known as "Self-Attention." It enables the model to weigh input elements based on their relevance for a given context.

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.

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.