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Universal Containers recently began a project to connect its ERP with Salesforce. One of the requirements is a daily batch process to create and update orders and order product information. The development team, using the corporate ETL tool, has created two processes to create these records using Bulk API. The test in the development environment worked fine, but in the production environment, some orderproduct records were not updated and showed an error "UNABLE_TO_LOCK_ROW:unable to obtain exclusive access to this record". There is one Process Builder on the Order Product object and no async process. Which two steps should a Solution Architect recommend to avoid this error? Choose 2 answers
Correct Answer: B,D
B) Sort the order product records by account and order before the Bulk API load. This answer can help avoid this error by reducing the chances of concurrent updates on the same parent record (account or order) by different batches. Sorting the records by account and order can ensure that all records related to a parent record are processed together in a single batch, which can prevent locking conflicts with other batches2. D) Add a retry process for the records rejected by this error. This answer can help recover from this error by attempting to update the rejected records again after some time interval. This can increase the likelihood of obtaining exclusive access to those records as they may be unlocked by then3. Sorting order product records by account and order before using the Bulk API can minimize the likelihood of row lock errors by ensuring that related records are processed in a sequence that reduces the chance of concurrent access attempts. Additionally, implementing a retry mechanism for records that encounter the "UNABLE_TO_LOCK_ROW" error provides a robust error handling strategy, allowing the system another opportunity to process the records successfully. These recommendations are in line with Salesforce's best practices for bulk data processing, which emphasize the importance of efficient data loading strategies and error management to ensure data integrity and system performance during large-scale data operations.
