To implement database sharding, you need to analyze the data schema and partition data into smaller chunks that can be distributed across multiple database instances. This approach improves performance and scalability. Using a single database instance is not sharding and doesn't improve scalability. Implementing a load balancer helps distribute requests but isn't the core of sharding. Using a NoSQL database is an alternative approach, not sharding itself.

Using the @Import annotation to modularize and group related auto-configurations is a recommended approach to manage and optimize a large Spring Boot application with extensive custom configurations. This reduces complexity while maintaining the flexibility of customizations. The other options are not practical or advisable approaches to handling auto-configurations in a large Spring Boot application.

In a large Spring Boot application with numerous endpoints, optimizing request mapping is crucial. Applying caching mechanisms (Option 3) can significantly reduce response times by caching the results of frequently accessed endpoints. While monitoring and documentation are essential, they don't directly optimize request mapping. Versioning (Option 4) is useful for maintaining backward compatibility but may not directly optimize request mapping. Swagger (Option 2) is valuable but more for documentation and testing.

To optimize the bean lifecycle in a large enterprise application, implementing the SmartInitializingSingleton interface allows you to defer time-consuming initialization tasks until all singletons are created, minimizing resource utilization during startup. The other options may help in specific cases but don't address the overall lifecycle optimization.

To execute a task immediately after the ApplicationContext has been started in a Spring Boot application, you can implement a custom event listener that listens for the ContextRefreshedEvent. This event is raised when the ApplicationContext is fully initialized and ready to use. You can then execute your task in response to this event.

In Spring Boot, you can handle validation errors by using BindingResult to capture errors during form submissions and then handle these errors in your controller. This approach allows you to provide custom error messages and logic for displaying errors to the user.

For a service to register itself with Eureka, it must have the @EnableDiscoveryClient annotation in its main application class. This annotation tells Spring Boot to enable service discovery and registration with Eureka. It is essential for ensuring that your microservices can be discovered and accessed by other services in the architecture.

Among the provided options, the @Autowired annotation is specifically used for injecting dependencies on setter methods in Spring. When you apply @Autowired to a setter method, Spring will automatically inject the required dependencies into that setter method. The other annotations have different purposes, such as @Inject and @Resource are more generic dependency injection annotations, and @Setter is not a standard Spring annotation for dependency injection.

The correct option is Option 2: Spring Boot Actuator. Spring Boot Actuator provides built-in production-ready features for monitoring and profiling Spring Boot applications. It exposes various endpoints that can be used for metrics, health checks, and application-specific information. These endpoints can be accessed via HTTP, JMX, or other protocols, allowing you to gather important insights into the behavior and performance of your Spring Boot application. Other tools like Visual Studio Code, Postman, and Apache Tomcat are not primarily used for profiling Spring Boot applications.

The correct format to specify the required authority using the @Secured annotation is @Secured("ROLE_ADMIN"). The "ROLE_" prefix is typically used to specify roles. The other options are not the correct format.