The Star Schema is a common data warehousing schema where a central fact table stores quantitative data, and dimension tables provide context and details about the data. This schema simplifies querying and reporting.

This database contains a mix of structured data (customer details, purchase histories, and product information) and semi-structured data (email communications). Semi-structured data is characterized by having some structure but also includes elements like emails, making it different from fully structured data.

Analysis of Variance (ANOVA) is a statistical test used when comparing the means of multiple groups. It assesses whether there are statistically significant differences between the group means, making option C the correct answer.

The attention mechanism in NLP allows a model to pay different amounts of attention to different words when processing a sequence. This mechanism is a fundamental component of transformer-based models like BERT and GPT, enabling them to capture contextual information and understand word relationships in sentences, paragraphs, or documents.

To retrieve all the records from a table named "Employees" in a relational database like MySQL, you would use the SQL command: SELECT * FROM Employees. The SELECT * statement retrieves all columns and rows from the specified table, effectively fetching all the records.

Ensemble methods in machine learning, such as bagging and boosting, aim to improve the generalization and robustness of models. They combine multiple models to reduce overfitting and improve predictive performance, making them a valuable tool for creating more accurate and reliable machine learning models.

Linear Regression is a commonly used algorithm for predicting continuous target variables. It establishes a linear relationship between the input features and the target variable, making it suitable for tasks like price prediction or trend analysis in Data Science.

In a data warehouse, the "Fact" table is used to store aggregated data at various levels of granularity. These tables contain measures or metrics, which are essential for analytical queries and business intelligence reporting.

Apache Flume is primarily used in the Hadoop ecosystem for data ingestion from various sources. It is a distributed, reliable, and available system for efficiently collecting, aggregating, and moving large amounts of data to Hadoop's storage or other processing components. Flume is essential for handling data ingestion pipelines in Hadoop environments.

Min-Max normalization can be sensitive to outliers. If outliers are present in the data, this scaling method can compress the majority of data points into a narrow range, making it less suitable for preserving the information in the presence of outliers. In scenarios where outliers are a concern, alternative scaling methods like Robust Scaling may be preferred.