Contract testing commonly uses Pact as a tool for validating API contracts. Pact is designed to ensure that the contracts between services are honored, enabling better communication and collaboration.

Contract testing in a microservices architecture is crucial for ensuring compatibility between services. It involves validating the agreements (contracts) between different services to ensure that they can communicate effectively without breaking each other's functionality. This helps maintain a reliable and stable system.

Dynamic rate limiting adjusts limits based on real-time metrics such as server load or other factors. By dynamically adjusting limits, APIs can handle varying levels of traffic and ensure optimal performance during peak times.

Error masking is an essential aspect of error handling that prevents exposing sensitive data through error messages. Instead of revealing detailed information about the internal system, error masking provides a generalized and user-friendly message while logging the detailed error information securely for system administrators. This practice helps in maintaining the security of the system by not divulging critical information to potential attackers or unauthorized users.

The HTTP method PUT is idempotent and used for updating resources in RESTful APIs. Idempotent means that making the same request multiple times will have the same effect as a single request.

In Postman, "Environments" are used to automate test runs across multiple environments. Environments allow you to set up different configurations and variables for your API tests, facilitating testing in various scenarios.

In this scenario, testing for fault tolerance becomes crucial. An API Gateway should gracefully handle failures in backend services to ensure continuous service availability. Fault tolerance testing assesses the system's resilience against potential failures and ensures uninterrupted service.

The 'Mock Server' feature in API testing tools, such as SoapUI or Postman, serves the purpose of simulating the behavior of a real server. It allows testers to conduct testing without relying on actual endpoints, facilitating early testing stages. This simulation helps uncover potential issues and ensures that the API functions as expected. It is particularly useful for testing scenarios where actual endpoints may not be readily available or stable.

Schema validation is essential in data-driven testing to verify that test cases can handle diverse API response formats. It ensures that the data structure conforms to the expected schema, enhancing the robustness of test cases.

Boundary value analysis is a testing technique that explores the behavior of a system at the edges or boundaries of the input domain. In the context of negative testing in APIs, it helps identify potential issues that may arise at the upper and lower limits of input values. By testing extreme values, testers can uncover vulnerabilities that might not be apparent with typical inputs.