The "Model Deployment" step in the Data Science Life Cycle is essential for taking the data science model from development to production. It involves integrating the model into real-world applications, making it a crucial phase.

XML and JSON are examples of semi-structured data. Semi-structured data is characterized by a hierarchical structure and flexible schemas, making it a middle ground between structured and unstructured data. It is commonly used in various data exchange and storage scenarios.

The process of transforming skewed data into a more Gaussian-like distribution is called "standardization." It involves shifting the data's distribution to have a mean of 0 and a standard deviation of 1, making it more amenable to certain statistical techniques.

Imputation with Mean is a common technique in Data Science to fill missing values by replacing them with the mean of the respective column. It helps maintain the integrity of the dataset by using the column's central tendency.

Data Loading is the process responsible for periodically loading fresh data into the data warehouse. It involves taking the data extracted from source systems, transforming it into the appropriate format, and then loading it into the data warehouse for analysis and reporting. Data Extraction, Transformation, and Integration are important steps in this process but are not solely responsible for loading data into the warehouse.

The primary purpose of activation functions in neural networks is to introduce non-linearity into the model. Without non-linearity, neural networks would reduce to linear regression models, limiting their ability to learn complex patterns in data. Activation functions enable neural networks to approximate complex functions and make them suitable for a wide range of tasks.

Supervised Learning is the type of learning that uses labeled data. In this approach, a model is trained on a dataset with known outcomes, allowing it to make predictions or classifications. It's commonly used for tasks like regression and classification in Data Science.

Data Visualizers are experts in creating insights and visualizations from raw data. They have a deep understanding of data visualization techniques, which is crucial for understanding audience preferences and viewing habits and making strategic decisions based on visualized insights.

The blank should be filled with "YARN." YARN (Yet Another Resource Negotiator) is responsible for resource management and workload monitoring in Hadoop clusters. It plays a crucial role in managing and scheduling jobs across the cluster.

Apache Kafka is more suitable for real-time data processing. It is a distributed streaming platform that can handle high-throughput, fault-tolerant, and real-time data streams, making it a popular choice for real-time data processing and analysis.