Cross-DBMS compatibility allows the automation framework to work seamlessly with multiple database management systems, such as MySQL and Oracle, ensuring consistent testing across different platforms. This feature enables the automation framework to handle the diverse database environments efficiently.

GDPR (General Data Protection Regulation) is a compliance standard that specifically focuses on protecting the privacy of individuals' personal data. It imposes strict requirements on organizations regarding the collection, storage, processing, and transfer of personal data to ensure individuals' rights are respected and their data is adequately protected. Compliance with GDPR is essential for organizations handling personal data of EU residents to avoid hefty fines and maintain trust with their customers.

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.

The SQL statement commonly used for testing the integrity of primary keys in a table is "ALTER TABLE ... ADD CONSTRAINT ... PRIMARY KEY". This statement is used to add a primary key constraint to a table, ensuring that the specified column(s) uniquely identify each row and enforcing data integrity. By adding a primary key constraint, it becomes impossible to insert duplicate or null values into the primary key column(s), thus maintaining the integrity of the data.

Common signs of SQL injection vulnerability include the presence of unsanitized user input directly concatenated into SQL queries, error messages that reveal database structure or query information, and the absence of input validation mechanisms. These indicators suggest that the system does not adequately sanitize and validate user input, making it vulnerable to SQL injection attacks.