Backup and recovery testing ensures that data can be successfully retrieved in case of a disaster, such as system failure, data corruption, or cyber-attacks. It involves testing backup procedures, data restoration processes, and disaster recovery plans to ensure they function effectively. This is crucial for maintaining data integrity and minimizing downtime in critical situations.

Data cleansing is about rectifying inconsistencies or anomalies in the data, ensuring data quality and integrity for accurate analysis and decision-making.

Using mock data for stress testing allows testers to simulate scenarios of high load and assess how the database performs under pressure. Stress testing helps identify potential bottlenecks, weaknesses, or points of failure in the database system, enabling teams to optimize performance and ensure reliability in real-world usage scenarios.

SQL injection attacks pose a significant threat to database security as they allow malicious actors to execute arbitrary SQL commands on the database. This can lead to unauthorized access to sensitive data, manipulation of data, or even complete database compromise. By injecting malicious SQL code into input fields, attackers can bypass authentication mechanisms and gain unauthorized access to the database, jeopardizing its confidentiality, integrity, and availability.

Query optimization plays a crucial role in SQL testing as it aims to enhance the performance of SQL queries by optimizing their execution plans. This involves identifying inefficient query execution paths and reorganizing or rewriting the queries to improve their efficiency. By optimizing SQL queries, it reduces the response time and resource utilization, thereby enhancing the overall performance of the database system.

Data cleansing is a critical step in the ETL process, where data is cleaned, standardized, and validated to ensure accuracy and consistency. This process improves the quality of data analysis and decision-making by providing reliable and trustworthy data. Testing data cleansing functionalities ensures that the ETL process effectively handles data quality issues and produces reliable results.

Partitioned indexing allows for efficient management of large datasets by dividing them into smaller, manageable chunks for faster access.

The data mapping document specifies the relationships between the source and target systems, guiding the data migration process by detailing how data from various sources will be mapped to the corresponding target.

The correct option is "Summary". Test summary reports play a vital role in communicating key information about the testing process to stakeholders. These reports typically include essential metrics, trends, and findings from the testing activities, providing stakeholders with valuable insights into the quality of the product and areas that require attention.

Testing large datasets presents scalability challenges, where the database system's ability to handle increasing amounts of data efficiently is put to the test. Scalability issues involve ensuring that the system maintains its performance and responsiveness as the dataset size grows, avoiding performance bottlenecks that can hinder overall database functionality.