Handling large data sets in a database often poses the challenge of scalability, where traditional database systems struggle to efficiently manage and process vast amounts of data. Scalability refers to the ability of a system to handle increasing amounts of workload or data without compromising performance or responsiveness.

The keyword commonly used in SQL to specify the order in which the result set should be returned is ORDER. This keyword is used in conjunction with ORDER BY clause in SQL queries to sort the result set based on one or more columns. By specifying the order, the database engine can efficiently retrieve and return the data in the requested sequence, potentially improving query performance.

NoSQL databases are often used for distributed data storage and retrieval in big data scenarios. Unlike traditional relational databases, NoSQL databases are designed to handle large volumes of unstructured or semi-structured data across distributed systems. They offer flexible data models, horizontal scalability, and high availability, making them well-suited for handling the complexities of big data environments. Examples of NoSQL databases include MongoDB, Cassandra, and HBase.

Mandatory Access Control (MAC) is commonly used in government and military systems. In MAC, access to resources is based on the security classification assigned to the user and the security classification assigned to the resource. Users are only able to access resources for which they have clearance. This model ensures that access is based on a need-to-know basis, as users can only access resources that are deemed appropriate based on their clearance level.

In a subquery, the EXISTS operator can be used to compare a single value with a result set. The EXISTS operator returns true if the subquery returns any rows, otherwise false. It is commonly used in correlated subqueries to check for the existence of a particular condition. For example, you can use EXISTS to check if there are any employees in a department with a certain job title.

In a database table, the column often used as the basis for creating an index is the primary key. A primary key uniquely identifies each record in the table, making it an ideal candidate for indexing to improve data retrieval speed. Indexing the primary key allows for fast lookup and retrieval of specific records based on their unique identifiers. Other columns, such as foreign keys, unique constraints, or timestamps, may also be indexed based on the specific requirements of the application and the types of queries being executed. However, primary keys are the most common choice for indexing in database tables.

Monitoring and profiling tools assist in database capacity planning by tracking resource usage and predicting future requirements. These tools monitor various aspects such as CPU utilization, memory usage, disk I/O, and network traffic to understand the current workload on the database server. By analyzing historical data and trends, database administrators can forecast future resource requirements and plan for capacity upgrades or optimizations to ensure optimal performance and scalability of the database system.

Indexing improves database query performance by speeding up data retrieval. It works by creating an optimized data structure that allows the database management system to locate rows more efficiently based on the indexed columns. This helps reduce the time required to execute queries, especially for large datasets, resulting in faster response times for users.

Implementing covering indexes ensures that all required columns for a query are included in the index itself, eliminating the need to access the actual table data for retrieval. This approach enhances query performance without affecting data insertion speed.

Authorization testing verifies that only authorized users have access to the database. It involves validating user credentials, permissions, and roles to prevent unauthorized access.