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.

Load testing is crucial for identifying performance bottlenecks in APIs as it simulates real-world usage, helping to evaluate system behavior under normal and peak load conditions.

Testing for Syntax Errors in a GraphQL API involves verifying that the API correctly handles requests with invalid or incomplete query structures. Syntax errors can occur when the queries do not adhere to the GraphQL language syntax, and detecting and handling these errors gracefully is crucial for a robust API.

Equivalence Partitioning is a testing technique where input data is divided into groups or categories, known as equivalence partitions. These partitions are treated the same by the API, ensuring that testing is representative of various input scenarios.

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.

Negative testing in API helps identify vulnerabilities and weaknesses in the system. It involves testing with invalid inputs, unexpected conditions, and error scenarios to ensure the API can handle such situations gracefully and report errors appropriately. This is crucial for enhancing the robustness and security of the API.

GraphQL APIs often pose challenges in testing due to nesting complexities. Nested queries can impact performance by introducing increased latency. This is a critical consideration for testers to ensure optimal API performance.