Hadoop Distributed File System (HDFS) is specifically designed for distributed storage and processing of large datasets across clusters of computers, making it well-suited for Big Data applications.

Bubble Sort is often used for sorting smaller lists due to its simplicity and ease of understanding. However, it may not be the most efficient for larger datasets. Quick Sort, Merge Sort, and Binary Search are more suitable for larger datasets and offer better performance.

YARN is a crucial component in the Hadoop ecosystem that separates resource management from job scheduling, enabling more efficient cluster utilization by various applications. It stands for Yet Another Resource Negotiator.

Recursive Feature Elimination (RFE) is an advanced technique used for automated feature selection in large datasets. It recursively removes less important features based on their contribution to model performance.

The correct command is 'git log.' This command displays a detailed history of changes, including commit messages, authors, dates, and other relevant information. The 'git history' and 'git show' commands do not provide the same level of detailed information.

In the dplyr package, the filter function is used for filtering rows based on a specified condition. It allows you to subset the data based on logical conditions, making it a powerful tool for data manipulation.

Storing a person's phone number typically requires a text data type like String since phone numbers can include special characters like dashes and parentheses. Integer is used for whole numbers, Float for decimal numbers, and Boolean for true/false values.

Cassandra is a NoSQL database designed for horizontal scalability and distributed architecture. It is suitable for handling large amounts of data across multiple commodity servers. MongoDB, Redis, and Couchbase are also NoSQL databases but may have different design considerations.

A Random Forest reduces variance by aggregating predictions from multiple decision trees. Each tree is trained on a different subset of the data, and their predictions are averaged, leading to a more robust and less overfit model compared to a single decision tree.

Imputation is a common strategy to handle missing values by replacing them with estimated values based on the available data. This ensures that the analysis is not compromised due to missing information.