The Pooling Layer in a CNN is responsible for reducing the spatial dimensions of the input. This layer downsamples the feature maps, which helps in retaining important features and reducing computational complexity.

HDFS (Hadoop Distributed File System) is the primary component in the Hadoop ecosystem for distributed data storage. It is designed to store large files across multiple machines and provides data durability and fault tolerance.

Text data from social media platforms is typically unstructured. It doesn't have a fixed format or schema. It may include text, images, videos, and other content without a well-defined structure, making it unstructured data.

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.

To ensure smooth routing of user requests to the correct model version in A/B tests, a feature flag system (option B) is commonly used. This approach allows controlled and dynamic switching of users between model versions. Randomly assigning users (option A) may not provide the desired control. Relying on user self-selection (option C) may lead to biased results, and IP-based routing (option D) lacks the flexibility and control of a feature flag system for A/B testing.

Unsupervised Learning is the most appropriate for clustering customers based on purchasing behavior. In unsupervised learning, the algorithm identifies patterns and groups data without any predefined labels, making it ideal for clustering tasks like this.

In MongoDB, the FIND command is used to query documents within a collection. It allows you to specify criteria to filter the documents you want to retrieve. MongoDB uses a flexible and powerful query language to find data in collections, making it well-suited for NoSQL document-based data storage.

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.

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.