For high-volume data loads using the Bulk API, monitoring should be performed programmatically by the orchestrating client—in this case, the custom Java application. The Bulk API is asynchronous, meaning that when you submit a job, Salesforce acknowledges the request and processes it in the background.
The Java application must actively track the state of its own jobs. Using the `getBatchInfo` (or `getJobInfo` in Bulk API 2.0) method allows the application to retrieve the real-time status of each batch. The application can check for statuses such as `Queued`, `InProgress`, `Completed`, or `Failed`. Once a batch is marked as `Completed`, the application can then call `getBatchResult` to retrieve a list of successes and failures for individual records.
Option B is architecturally unsound because Bulk API operations are designed to bypass most synchronous Apex logic to ensure performance; furthermore, creating custom records for every error in a "nightly batch load" would likely hit other platform limits (like storage or CPU) and defeat the purpose of using the Bulk API. Option C is ineffective for Bulk API monitoring, as debug logs do not capture the background processing of bulk batches and would quickly hit the log size limits.
By recommending Option A, the architect ensures that the Java application maintains full control over the integration lifecycle. The application can log errors locally, implement automated retries for transient failures, and provide the CIO with accurate, high-level reporting on the success rate of the nightly loads without placing unnecessary overhead on the Salesforce platform.
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