Word2Vec is a widely used NLP model that captures word context by representing words as vectors in a continuous space. It preserves the semantic meaning of words, making it a powerful tool for various NLP tasks like word embeddings and text analysis. The other options are not NLP models and do not capture word context in the same way.

Data augmentation involves applying spatial transformations to input images, such as rotation, flipping, or cropping, to increase the diversity of the training data. This technique enhances model generalization and performance.

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.

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.

For real-time analysis on streaming data, Apache Spark is a powerful tool. It provides excellent performance capabilities and extensive libraries for stream processing, making it suitable for handling and analyzing large volumes of data in real-time.

In a normal distribution, approximately 95% of the data falls within two standard deviations of the mean. This is a fundamental property of the normal distribution, as specified by the Empirical Rule or the 68-95-99.7 rule, which describes the percentage of data within one, two, and three standard deviations of the mean.

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.

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 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.

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.

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.

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.