Common data sources in data-driven testing include CSV files, allowing for easy organization and modification of test data without altering the test script, leading to greater flexibility and scalability.

In a lightweight mobile application, where efficiency and simplicity are crucial, RESTful APIs are well-suited. They offer a stateless communication model, enabling lightweight data exchange with minimal overhead. Other options like GraphQL, SOAP API, and WebSockets might introduce unnecessary complexity for such scenarios.

GraphQL's single endpoint approach can increase the attack surface in security testing. While it simplifies testing, it also introduces the risk of more concentrated attacks on a single endpoint, requiring thorough vulnerability assessments to ensure robust security measures.

API testing tools in Agile environments should facilitate easy script maintenance. This ensures that the tests can be quickly adapted to accommodate frequent changes in requirements, a key characteristic of Agile development. Easy maintenance contributes to the flexibility and responsiveness required in Agile workflows.

In GraphQL, the 'Schema' plays a critical role in API design by defining the data structure, types, and relationships within the API. It acts as a contract between the client and server, specifying what data can be queried and how it relates. The schema ensures consistency and clarity in communication between the client and server, making it a fundamental aspect of GraphQL API design.

Latency Testing would be the primary focus to identify delays in response times. It helps pinpoint specific performance issues related to delays in data processing or communication. Baseline Testing establishes a performance baseline for comparison, Regression Testing ensures new changes don't adversely affect performance, and Soak Testing assesses system stability over an extended period but may not be as effective in identifying latency issues.

API testing in Agile thrives on collaboration with development teams. This close collaboration ensures that the API functionality aligns with user stories and meets the expectations of the development and testing teams. Effective communication and collaboration are essential components of successful Agile API testing.

Performance testing is crucial in Continuous Deployment (CD) pipelines, ensuring that the API can handle the expected load and maintain optimal performance. It helps identify and address scalability issues before deploying changes to production.

Performance testing of individual microservices is critical as it helps detect potential dependencies and communication issues. The impact on the overall system is significant because poor performance in one service can affect the entire architecture, leading to latency and reliability issues across the microservices ecosystem.

APIs commonly use HTTP headers like 'X-RateLimit-Limit' to convey rate limit information to clients. This approach is efficient and aligns with HTTP standards.