C


When moving a large number of records from an external ERP system into Salesforce using the Bulk API, a data architect must consider factors like data integrity, system performance, and the avoidance of record locking issues. The Bulk API can be used in either serial or parallel mode. In serial mode, batches are processed one at a time, which can be slower but significantly reduces the risk of record locking, especially in complex data models.

Let's evaluate the given options in this context:

A. Placing 20 batches on the queue for upsert jobs: While this approach is feasible, it doesn't specifically leverage the benefits of using the Bulk API in serial mode. The number of batches placed in the queue might not be the most relevant factor in terms of efficiently managing data load and avoiding locking issues.

B. Inserting 1 million orders distributed across a variety of accounts with potential lock exceptions: This option seems to overlook the key advantage of using serial mode, which is to minimize lock exceptions. When dealing with a high volume of related records (like orders associated with accounts), parallel processing can lead to record locking issues. However, this option does not mention any strategy to mitigate these issues.

C. Leveraging a controlled feed load with 10 batches per job: This approach suggests a more controlled and deliberate loading strategy, which is beneficial when using the Bulk API in serial mode. By carefully managing the number of batches and their size, it’s possible to reduce the risk of system overload and lock exceptions, ensuring a smoother data migration process....

Option C, which recommends leveraging a controlled feed load with 10 batches per job, is a more practical approach. It advocates breaking the data into smaller batches, which helps maintain performance, reduces locking conflicts, and provides better manageability during the data migration process. This is why option B is not the recommended choice for this scenario.