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.

Versioning in APIs is crucial for maintaining backward compatibility. When older versions are still supported, existing clients can continue to function without disruption. This approach allows a gradual transition to newer versions, preventing a sudden break in functionality for users relying on older API versions. It also provides flexibility for clients that may not be able to update immediately.

Dynamic data handling is crucial for API test reliability in advanced data-driven scenarios as it effectively manages evolving API responses. APIs often produce dynamic data, such as timestamps or unique identifiers, which can impact test outcomes. By handling dynamic data appropriately in the test scripts, the reliability of API tests improves. This ensures that the tests remain accurate and consistent, even when dealing with varying responses from the API, ultimately contributing to a more reliable testing process.

Incorporating delays between service interactions is a key factor in designing a data-driven test for an API that interacts with multiple external services. This helps mimic real-world scenarios where different services may respond at varying speeds, ensuring data variability and accuracy in the test. Static data may not capture these dynamics, randomizing data for each call may introduce unpredictability, and using predetermined data for all interactions may not reflect real-world conditions.

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.

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.