In Gradient Boosting, the learning rate (Option A) is adjusted at each step to minimize residual errors. A smaller learning rate makes the model learn more slowly and often leads to better generalization, reducing the risk of overfitting.

Gradient clipping is a technique used to mitigate the gradient explosion problem in deep learning. It limits the magnitude of gradients during training, preventing them from becoming too large and causing instability.

In image processing, adjusting the contrast or brightness of an image is termed as "Enhancement." Image enhancement techniques are used to improve the visual quality of an image by enhancing specific features such as brightness and contrast.

Data Preprocessing is the process of cleaning, transforming, and organizing raw data to prepare it for modeling. It includes tasks such as handling missing values, feature scaling, and encoding categorical variables. This step is crucial in Data Science to ensure the quality of data used for analysis and modeling.

The AUC-ROC curve measures classification performance at various threshold levels. It represents the trade-off between true positive rate (Sensitivity) and false positive rate (1 - Specificity) at different threshold settings. The threshold affects the classification decisions, and the AUC-ROC summarizes this performance.

Sentiment Analysis is the NLP task most relevant for categorizing customer reviews as positive, negative, or neutral. It involves assessing the sentiment expressed in the text and assigning it to one of these categories based on the sentiment polarity. NER, Text Summarization, and Machine Translation serve different purposes and are not suitable for sentiment categorization.

The algorithm used for this purpose is Random Forest. It's an ensemble learning method that builds multiple decision trees and aggregates their results. As the data is split into subsets, the decision tree is developed incrementally, making it a powerful algorithm.

In MongoDB, the $regex operator is used to test a regular expression against a string. It allows you to perform pattern matching on string fields in your documents. This is useful for querying and filtering data based on specific patterns or text matching requirements.

A Star Schema is the best choice for a data warehouse designed for complex analytical queries. It provides a denormalized structure that optimizes query performance. Snowflake Schema is similar but more normalized. Factless Fact Table is used for scenarios without measures. NoSQL databases are not typically used for traditional data warehousing.

Data Visualization is the technique used to understand the relationships between variables in a dataset. This involves creating scatter plots, correlation matrices, and other visual representations to identify patterns and correlations in the data, which is an essential part of Exploratory Data Analysis (EDA).