ETL stands for Extract, Transform, Load. It is a crucial process in data warehousing and database testing where data is extracted from various sources, transformed according to business rules, and loaded into a target database or data warehouse for analysis and reporting purposes.

It's important to ensure that test data generation tools comply with data privacy regulations when handling sensitive information. Compliance with privacy regulations ensures that sensitive data is handled appropriately and securely during the testing process.

Database authorization is the process of granting or denying access to specific data based on the user's role and permissions. It ensures that only authorized users can access the data they are allowed to see, thus making option 3 the correct choice.

Authorization testing focuses on evaluating whether users have the necessary permissions and privileges to access specific resources or perform certain actions within the system.

Data reconciliation in data consistency testing refers to the process of identifying and resolving inconsistencies between different data sets. This involves comparing data from various sources to ensure alignment and accuracy, thus maintaining data integrity.

Upon encountering data corruption in the target system, the next step in the testing process should be to perform data validation. Data validation involves verifying the accuracy, completeness, and consistency of migrated data against predefined criteria and expectations. This step helps identify any discrepancies or anomalies caused by data corruption and ensures the integrity of financial data. Additionally, analyzing data transformation and loading processes can help pinpoint the source of corruption and prevent its recurrence in future migration attempts. Restoring data from backup may be necessary if the corruption is severe, but it should be complemented with thorough data validation to confirm the integrity of the restored data. Updating the data migration plan may also be required to incorporate lessons learned from the encounter with data corruption and improve future migration efforts.

Biometric authentication, single sign-on, and role-based access control (RBAC) are common authentication methods used in applications. Captcha verification is primarily used to prevent automated access by bots.

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.

SQL Injection is a security vulnerability where attackers insert malicious SQL code into input fields, such as login forms or search queries, to manipulate the database and perform unauthorized actions. This vulnerability can lead to data breaches, data loss, or unauthorized access to sensitive information stored in the database. Preventative measures include parameterized queries, input validation, and using ORM frameworks.