Machine Learning Engineers are responsible for developing and deploying machine learning models into production systems. They work closely with Data Scientists who create the models but specialize in the deployment process.

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 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.

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 this scenario, you would use a content-based recommendation approach. It recommends items (in this case, movies) based on their content or features, such as genre, actors, and plot. Collaborative filtering and hybrid systems focus on user behavior and preferences, while popularity-based recommendations don't consider movie content.