Data-driven testing positively impacts the maintainability of test scripts by reducing code duplication. When different sets of test data are separated from the test scripts, changes in data won't necessitate modifying the entire script. This promotes cleaner, more modular code, making it easier to manage and maintain. As a result, the overall test script becomes more robust and adaptable to changes in API functionalities or data inputs.

Rate limiting enhances security by mitigating Denial of Service (DoS) attacks. It restricts the number of requests an API can receive, preventing overload and potential attacks.

OAuth can be integrated with existing identity providers by using its authorization grant flow, allowing secure and standardized authentication.

Scalability is a critical factor for maintaining API stability and reliability in the context of big data. Ensuring that the API can handle increased load and data volume is essential to prevent performance issues and downtime.

Throttling in the context of API rate limiting refers to limiting the speed of incoming requests from a client. It involves controlling the rate at which requests are processed to prevent overloading the server and ensure fair usage among all clients.

API testing in CI/CD is crucial for early detection of integration issues, ensuring that changes do not disrupt the continuous deployment pipeline. It focuses on validating communication between different components. User interface testing is distinct, and API testing primarily deals with backend functionality. Database schema changes are typically verified separately. Code coverage is more related to unit testing rather than API testing.

API documentation serves as a common reference point for developers and testers, fostering a shared understanding of API functionalities. It includes details about endpoints, request formats, and expected responses, enabling both parties to align their understanding of the API's behavior. This shared knowledge reduces misunderstandings and communication gaps, leading to smoother collaboration between development and testing teams.

Testing values at the edges of input ranges is crucial in API testing to identify boundary-related issues. APIs often have specific boundary conditions that, if not tested thoroughly, can lead to unexpected behavior or vulnerabilities. This ensures that the API functions correctly at the limits of its acceptable input, enhancing overall system reliability.

In handling large data sets, the API's ability to filter effectively is crucial for maintaining optimal performance. Filtering allows selective retrieval of data, reducing unnecessary data transfer.

A proactive approach to error handling involves identifying and addressing potential issues before they become critical. In the context of API development, a proactive strategy enables developers to trace the root cause of issues more efficiently. Therefore, option b) "Proactive" is the correct choice.