SF-Data-Arch Salesforce Data Management Practice Question
Universal Containers is importing 5 million child records into a custom object using the Bulk API in parallel mode. The load frequently fails due to UNABLE_TO_LOCK_ROW errors because many child records share the same parent account. Which strategy should the architect recommend to resolve this failure?
⚠ Common exam trap
Candidates often try to increase batch sizes to speed up loads, which actually exacerbates row-locking issues when many records share the same parent account ID.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Sort the CSV file by ParentId and process the data load in serial mode.
Bulk API parallel processing often triggers lock contention when multiple batches attempt to update different child records that roll up to the same parent record simultaneously. By organizing the CSV file such that records with the same parent are grouped together and then processing the load in serial mode, the system ensures that only one batch accesses a parent at a time.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Sort the CSV file by ParentId and process the data load in serial mode.
Why this is correct
Sorting the CSV by ParentId and switching to serial mode effectively eliminates lock contention. While serial mode is slower than parallel, it prevents the failures caused by concurrent batches trying to update the same parent record, ensuring that the entire multi-million record data load completes successfully without manual intervention.
- ✗
Disable all triggers and validation rules on the child object during the import.
Why it's wrong here
Disabling triggers and validation rules may improve the speed of the data load by reducing the execution time per record. However, it does not address the fundamental issue of record locking on the parent account record, which is the primary cause of the specific locking errors reported.
- ✗
Increase the batch size to 10,000 records per batch in the Bulk API settings.
Why it's wrong here
Increasing the batch size actually increases the likelihood of lock contention because each batch remains active for a longer duration. Larger batches holding locks on the same parent record for extended periods will cause other concurrent batches to time out while waiting for those locks to be released.
- ✗
Enable PK Chunking to split the data load into smaller, manageable pieces.
Why it's wrong here
PK Chunking is specifically designed to handle large data volume queries rather than data loads or inserts. While it helps in exporting millions of records efficiently, it does not provide a mechanism to prevent parent record locking errors during an insert or update operation within the Bulk API.
Visual reference
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Salesforce exam blueprint
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