Option 2: Using a mix of scheduling algorithms based on process types is the most effective approach. For CPU-bound processes, a scheduling algorithm like Shortest Job Next (SJN) or Shortest Remaining Time First (SRTF) can be used to minimize waiting times and increase CPU utilization. For I/O-bound processes, a scheduling algorithm like First-Come, First-Served (FCFS) or Round Robin can be utilized to ensure fairness and efficient I/O utilization. This balanced approach optimizes resource usage for both types of processes.

Option 1 is correct. For a banking system, ACID-compliant transactions ensure data integrity, consistency, and security. Separate tables for accounts, transactions, and users allow for efficient data management and auditing. Distributed transactions and blockchain are not typically used in traditional banking systems due to complexity and regulatory concerns. Storing transactions in flat files lacks transactional integrity and is not suitable for secure financial data. While encryption is essential, it's a security measure rather than a primary transaction management method. Normalization is important but not the primary focus when dealing with financial transactions.

The main goal of debugging is to identify and fix defects, errors, or bugs in the code. It involves the process of analyzing code behavior, locating the source of issues, and making necessary corrections to ensure that the software functions correctly. Debugging is crucial for maintaining code quality, enhancing software reliability, and delivering a stable product to end-users.

CPU scheduling in operating systems involves managing the allocation of CPU resources to different processes. This ensures that the CPU is utilized efficiently by executing tasks in an optimized manner.

The Least Recently Used (LRU) algorithm selects the page for replacement that has not been accessed for the longest time among all pages in memory. It operates on the principle that pages that have not been accessed recently are less likely to be accessed in the near future compared to recently accessed pages. This helps in optimizing memory usage by keeping frequently accessed pages in memory.

Virtualization technology primarily aims to consolidate multiple physical servers into one, allowing for more efficient resource utilization and reduced hardware costs. This facilitates easier management and scalability.

Monitor: This option refers to a synchronization construct that allows threads to wait until a condition is true before proceeding. Monitors provide a higher level of abstraction compared to mutexes and semaphores, as they encapsulate both the data being protected and the synchronization mechanisms within a single construct. They help in preventing race conditions and ensuring thread safety in concurrent programming.

Regression testing involves re-running test cases to ensure that new changes haven't adversely affected existing functionalities. It verifies that the code behavior remains unchanged post-modifications. This is crucial in software development to catch bugs introduced inadvertently during updates and to maintain overall system reliability and stability. It also helps in ensuring that new features don't break existing functionalities.

The Adapter design pattern allows objects with incompatible interfaces to work together. It acts as a bridge between the client and the target class, enabling communication and interaction. This pattern is useful when integrating existing systems that have incompatible interfaces or when designing reusable libraries that need to work with multiple interfaces.

Graph databases are ideal for handling complex relationships as they excel in managing interconnected data. Nodes represent entities like users, and edges represent relationships between them. This structure is well-suited for social networks where users have varied connections. Document databases are better suited for semi-structured data and may not handle complex relationships as effectively as graph databases. Columnar databases are optimized for analytical queries, not for complex relationship handling. Key-value databases are efficient for simple data retrieval but lack the structure needed for complex relationships.