The immediate step to enhance database security is to configure the database server to use SSL/TLS encryption for communication. This ensures that data transmitted between clients and the server is encrypted, mitigating the risk of exposure during transmission. Updating the software and implementing strong password policies are important security measures but do not directly address the communication vulnerability. Physical security measures are essential but do not pertain to securing communication protocols.

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.

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.

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.

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.

In a blind SQL injection attack, attackers may exploit error messages or use comments to gather information. However, a common technique is using database metadata to understand the database structure.

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.

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.

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 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.

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.

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