In Spring Boot, conflicts between properties defined in the application properties file and environment variables are resolved by giving precedence to environment variables. This means that if a property is defined in both places, the environment variable value will override the value in the properties file. This behavior is designed to make it easier to configure applications in different environments using environment variables.

Backpressure in Reactive Streams is a mechanism to deal with situations where a subscriber can't keep up with the rate of data emitted by the publisher. It allows the subscriber to signal the publisher to slow down or stop emitting data temporarily. Spring Boot handles backpressure by allowing subscribers to request a specific number of items they can handle, and the publisher will respect this request, preventing data loss or overwhelming the subscriber.

Optimizing the performance of RESTful APIs in a Spring Boot application involves several strategies, including implementing caching mechanisms to reduce redundant requests, using pagination to limit the amount of data returned, and optimizing individual endpoints by reducing unnecessary processing and database queries. These strategies collectively enhance API response times and scalability, providing a better experience for API consumers.

To implement a custom caching strategy in Spring Boot, you can extend the @Cacheable annotation with custom logic. This allows you to define your own caching behavior tailored to your application's specific requirements without modifying the core Spring Boot code. Modifying core code or using unsupported third-party libraries is not recommended, and disabling caching is counterproductive to the goal of caching in a Spring Boot application.

In a Spring Boot project, the pom.xml file is primarily used to declare project dependencies when using Maven as the build tool. This XML configuration file contains information about project metadata and dependencies, making it essential for managing project dependencies and ensuring proper version control. The other options are not used for dependency management in Spring Boot projects.

The main goal of Reactive Streams in Spring Boot is to provide a framework for building non-blocking, reactive applications. Reactive Streams are designed to handle asynchronous data flows with a focus on low-latency, high-throughput processing. They enable developers to write code that reacts to data as it becomes available, which is essential for creating responsive and scalable applications, particularly in scenarios with high concurrency or streaming data.

Configuring a cache in a Spring Boot application primarily aims to improve application performance. Caching helps store frequently accessed data in memory, reducing the need to fetch it from the database repeatedly. This optimization can significantly speed up application response times. The other options do not reflect the primary purpose of caching.

To ensure data integrity between the cache and the underlying data source in a Spring Boot application, a write-through caching strategy with cache synchronization is effective. This approach ensures that any changes made to the data source are also reflected in the cache in real-time. Options 2, 3, and 4 are not recommended practices for maintaining data integrity between the cache and the data source.

The primary advantage of using reactive programming in Spring Boot applications is improved developer productivity. Reactive programming enables developers to write more concise and expressive code for handling asynchronous and event-driven scenarios. It simplifies complex, non-blocking operations, making it easier to work with asynchronous data streams and events, leading to more efficient and maintainable code.

In this scenario, you would use unit testing with mocked dependencies to isolate and test individual components of the microservice. This helps ensure that each component functions correctly in isolation. Integration testing with real external services can introduce complexity and is not suitable for ensuring the correctness of interactions among components. Manual testing and ignoring component interactions are not effective strategies.