Mocking APIs helps simulate the behavior of external services or components, allowing developers to test their code in isolation and ensure proper integration without relying on the actual services.

Measuring throughput is crucial when testing for peak load, as it reflects the maximum number of requests an API can handle efficiently. Throughput indicates the system's processing capacity under heavy loads, providing insights into its scalability and performance.

OAuth 2.0 introduced a shift from signatures to tokens for better scalability and simplicity. It relies on access tokens rather than cryptographic signatures for security.

Load testing is essential for evaluating the performance of an API under various levels of load. It helps identify bottlenecks and weaknesses in the system when subjected to sustained use and heavy loads.

In a CI/CD pipeline, API tests are typically executed after the Deployment stage. This is because, during the Deployment stage, the application is deployed to a testing environment, allowing for comprehensive API testing to ensure the proper integration of components. API tests at this stage help catch integration issues before they progress further in the pipeline.

RESTful APIs commonly use JSON for data exchange, providing a lightweight and easy-to-read format. SOAP APIs, on the other hand, typically use XML for data exchange, which is more rigid and structured.

Boundary Value Analysis involves testing the boundary or extreme values of input. For financial transactions, key values to test would be just below and above the transaction limit. In this scenario, testing $4999.99 and $5000.01 ensures accurate handling of transaction limits.

To ensure a new feature meets both functional and performance requirements, developers and testers should collaborate from the start. This involves defining performance requirements together, allowing for early identification and resolution of any issues. This collaborative approach ensures a more robust and well-rounded feature.

Data-driven testing, in the context of API testing, assists in maintaining scalability by enhancing reusability. As the complexity of the API increases, using data-driven testing allows the same set of test scripts to be reused with different sets of data. This promotes efficiency and reduces the effort needed to handle varied scenarios. By parameterizing the inputs, the testing process becomes more adaptable and scalable, ensuring effective API testing in dynamic environments.

In API testing, data-driven testing is crucial for handling changing data inputs. It allows the automation of tests with various data sets, enhancing test coverage and effectiveness. Tools that support this feature enable testers to parameterize data and execute tests across multiple scenarios.