Trees find wide applications in real-world scenarios due to their hierarchical structure. They are used to represent data in a way that reflects natural relationships, making them ideal for tasks like organizing files, managing organizations, and structuring databases efficiently.

Normalization is the process of organizing data in a database to reduce redundancy and improve data integrity. It involves breaking down large tables into smaller, more manageable tables and defining relationships between them. Denormalization, on the other hand, involves combining tables to improve query performance but may lead to data redundancy. Partitioning and sharding are techniques used for distributing large databases across multiple servers. While both normalization and denormalization involve restructuring tables, normalization is specifically focused on reducing redundancy and improving data integrity, making it the correct choice.

Statelessness is a fundamental constraint in RESTful APIs that requires each request from a client to contain all the necessary information for the server to process it, without relying on any stored server state. This enables scalability, reliability, and better performance as servers can handle requests independently without retaining client-specific information between requests.

To revert a commit in Git without losing the changes, you can use the git revert command. Unlike git reset, which can remove commits and potentially lose changes, git revert creates a new commit that undoes the changes introduced by a specific commit. This maintains a clean history and preserves the changes made in subsequent commits. The git revert command is especially useful when working in a collaborative environment where rewriting history can cause conflicts for other team members. It allows for a safer way to undo changes while keeping the project history intact.

The Decorator design pattern allows behavior to be added to individual objects dynamically, while inheritance creates a static hierarchy of classes where subclasses inherit behavior from their parent class. The Decorator pattern promotes code reusability and flexibility.

In multitasking operating systems, the mechanism used to switch between processes is known as a context switch. A context switch involves saving the state of a currently running process, including its registers, program counter, and other relevant information, and then loading the state of the next process to be executed. This allows the operating system to efficiently allocate CPU time among multiple processes, enabling concurrent execution and multitasking capabilities. Understanding context switching is crucial for system performance optimization and multitasking efficiency.

The "Optimal Substructure" property in dynamic programming states that the optimal solution to a problem can be constructed from optimal solutions to its subproblems. This property enables subproblems to be solved independently and then combined to solve the overall problem efficiently.

Authentication involves verifying the identity of users or devices, ensuring they are who they claim to be. Authorization, on the other hand, deals with granting or denying access to resources based on the authenticated identity and specified permissions. While authentication establishes trust, authorization controls the level of access granted.

Dynamic programming is based on the principle of breaking down complex problems into smaller, manageable subproblems and solving each subproblem just once, storing the results for future reference. Memoization is a technique used in dynamic programming to store previously computed results to avoid redundant computations, making it an essential aspect of this approach.

Maintaining ACID properties in distributed databases involves trade-offs compared to centralized databases. One major trade-off is the balance between consistency and availability. In distributed systems, ensuring strong consistency across all nodes can lead to increased latency and potential scalability challenges due to the need for synchronous communication and coordination. On the other hand, prioritizing availability may sacrifice consistency, leading to eventual consistency models where data may be temporarily inconsistent across nodes. Additionally, network partitioning issues can arise in distributed databases, affecting the system's ability to maintain ACID properties uniformly. These trade-offs require careful consideration and architectural decisions based on the specific requirements of the application and the desired level of data consistency and availability.