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.

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.

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.

Ensemble methods, like Random Forest and Gradient Boosting, combine multiple models (decision trees in the case of Random Forest) to improve overall predictive performance. These models are trained independently and then aggregated to make predictions. The combination of models is what enhances the accuracy and robustness of the ensemble.

A formal review is a structured evaluation process aimed at identifying inconsistencies between a work product and its input criteria, which can include requirements, standards, or specifications. It helps ensure the quality and correctness of the work product.

The Adapter design pattern enables compatibility between two incompatible interfaces by creating a new interface (the adapter) that acts as a bridge between the two. This adapter converts the methods of one interface into methods that the other interface can understand, making them compatible without changing their source code.

System testing concentrates on ensuring that individual components within the system behave correctly as a whole and meet user requirements. Integration testing, on the other hand, is specifically focused on verifying the interactions and data flow between different modules and their compatibility.

A well-structured regression testing plan would have prevented issues after a minor patch by retesting the entire software. This ensures that not only the newly patched code is checked but also that it doesn't break any previously stable functionalities. Ignoring the patch (option 4) or only testing the new functionality (option 1) would not provide adequate coverage.

System testing is essential to ensure that the software behaves as intended and meets user requirements before it's released into the production environment. While other options like identifying bugs and hardware compatibility are important, the primary purpose of system testing is to validate the software's compliance with user expectations.

Convolutional Neural Networks (CNNs) are well-suited for translation-invariant tasks, such as image processing, due to their ability to capture local patterns and features. CNNs can automatically learn and detect features in images, making them effective for tasks like object recognition and image classification.

To reduce the impact of extreme values in income data, a log transformation is often used. It compresses the range of values and makes the distribution more symmetrical. Min-Max scaling and z-score standardization don't address the issue of extreme values, and removing outliers may lead to loss of important information.

Pre-processing in computer vision typically includes steps like resizing, filtering, and transforming an image. It's during this phase that an image is converted into an array of pixel values, making it ready for subsequent analysis and feature extraction.