gRPC’s use of Protocol Buffers (protobuf) provides a strongly-typed contract for services and enables efficient binary serialization. This is a key advantage over REST/JSON for inter-data-center communication where performance and bandwidth matter. While gRPC does have features related to load balancing, they are not its primary differentiator in this scenario, and it does not restrict language choice nor use JSON as its primary payload format.

The MicroProfile Rest Client specification allows developers to define type-safe RESTful web service clients via annotated Java interfaces. Using `@RegisterRestClient` (to make it a CDI bean) and `@Inject @RestClient` to inject it provides a declarative, type-safe, and boilerplate-free method that integrates perfectly with Helidon MP’s CDI container. The other options are more manual, less integrated, or violate the principles of type-safe dependency injection.

In Helidon SE, `Rules.contentType()` acts as a request predicate or filter. The route `Rules.contentType(“application/json”)` requires the request to have a `Content-Type` header of `application/json`. However, the `Accept` header is for response negotiation, not request content. Since a GET request typically has no body, it lacks a `Content-Type` header, so the predicate fails. The route is therefore not matched, and the routing continues, likely resulting in a 404 if no other route matches.

For long-running, read-heavy operations, loading full entity graphs can cause memory bloat. The most effective strategy is to be selective about data fetched (using DTOs or scalar results) and to mark queries/entities as read-only where possible, which can allow the persistence provider to optimize caching. Periodically clearing the persistence context (`EntityManager.clear()`) is also a valid part of this pattern to detach old entities. Simply increasing heap size is a workaround, not a fix. Marking the transaction as SUPPORTS may not start a transaction at all, which is inappropriate for a process that needs consistent data reads.

CDI allows for fine-grained event selection using qualifier annotations. You define a custom qualifier (e.g., `@Completed`) and annotate the event object when it is fired (`event.fire(new OrderEvent())`). The observer method’s parameter is then annotated with both `@Observes` and the same qualifier `@Completed`. This is the standard CDI pattern for filtering events. There is no built-in `orderStatus` attribute for `@Observes`, and `Async` relates to transaction phase, not event data filtering.

Helidon SE’s health support is designed to run health checks in parallel. By registering separate `HealthCheck` instances for each dependency, the slow payment API check will not block the fast database check. The overall health response time will be dictated by the slowest check, but the system remains more resilient and diagnosable than if they were forced to run sequentially. Caching (option 4) can be an additional optimization but isn’t the primary mechanism Helidon provides for handling slow checks.

In reactive programming, blocking operations (like CPU-intensive work) must not be performed on the main event loop thread. The `map` and `flatMap` operators by default execute on the calling thread. The correct pattern is to use an operator like `observeOn` (or its equivalent in the framework’s API, such as `runOn` in Helidon’s Reactor implementation) to signal that subsequent operations should be scheduled on a specified executor service (like a bounded elastic thread pool). Then, the `map` operator can be safely used.

The MicroProfile Config specification defines a hierarchy for configuration sources. System properties have a higher ordinal (priority) than properties files. Setting it via `-D` in the Maven Surefire plugin configuration is a clean, standard, and isolated way to override configuration for tests without polluting main resources or test resources. While a test resource file (option 3) can work, system properties are more explicit and have higher precedence, ensuring the override is always applied. Modifying main resources is error-prone, and hard-coding is an anti-pattern.

In MicroProfile Fault Tolerance, annotations compose. The `@Timeout` starts a timer for the method execution. If it expires, a `TimeoutException` is thrown. This exception is a failure from the perspective of both the `@Retry` policy (which may trigger a retry) and the `@CircuitBreaker` (which records the failure). The `@CircuitBreaker` monitors the failure rate over time; consecutive failures (including timeouts and IOExceptions) can cause it to open, which will then fail-fast subsequent calls, overriding the `@Retry` policy until the circuit is half-open or closed again.

gRPC’s use of Protocol Buffers (protobuf) provides a strongly-typed contract for services and enables efficient binary serialization. This is a key advantage over REST/JSON for inter-data-center communication where performance and bandwidth matter. While gRPC does have features related to load balancing, they are not its primary differentiator in this scenario, and it does not restrict language choice nor use JSON as its primary payload format.0