According to the official Salesforce guide1, using Bulk API serial mode for data migration can help avoid locking issues by processing batches in a single thread. This mode ensures that batches are processed in the order they are received and that only one batch is processed at a time. This reduces the risk of lock contention and deadlocks, especially when updating parent and child records in a lookup relationship. Option B is the correct answer because it suggests using Bulk API serial mode for data migration. Option A is incorrect because using Bulk API parallel mode for data migration can cause locking issues by processing batches in multiple threads. This mode does not guarantee the order of batch processing and can result in concurrent updates to the same records, which can lead to lock contention and deadlocks. Option C is incorrect because inserting the order in another custom object and using Batch Apex to move the records to Service_Order__c object adds unnecessary complexity and overhead to the data migration process. Option D is incorrect because changing the lookup configuration to "Clear the value of this field" when lookup record is deleted does not address the locking issues caused by data migration, but rather by record deletion.

When denormalizing a data model into a single object with a picklist field, the data architect should consider the Bulk API limitations on picklist fields and the standard list view in-line editing. The Bulk API has a limit of 1,000 distinct picklist values per file1, which could be an issue if there are more than 1,000 venues in the future. The standard list view in-line editing allows users to edit multiple records at once, which could introduce data quality issues if the venue picklist is not validated or restricted2. The other options are not relevant to denormalizing a data model.

Data skew occurs when a large number of child records are associated with a single parent record, or when a single user owns a large number of records. This can cause performance issues and lock contention. To avoid data skew, the data architect should limit assigning one user 10,000 records ownership, limit associating 10,000 opportunities to one account, and limit associating 10,000 records looking up to the same record

Loading new records via the Insert operation and existing records via the Update operation will allow using the external ID field as a unique identifier and avoid any duplication or overwriting of records. This is faster and safer than deleting all existing records or using the Upsert operation, which might cause conflicts or errors
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