SF-Data-Arch Large Data Volume Considerations Practice Question
A company is importing 50 million records into a custom object. Which strategy should be used to minimize record locking contention during the high-volume insert operation?
⚠ Common exam trap
Candidates often recommend parallel processing to speed up imports, completely ignoring how parallel threads exacerbate row-level locking on shared records.
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 data by OwnerID and process records in serial mode.
Minimizing locking contention requires optimizing the database interaction patterns within Salesforce. Serializing record processing and organizing batches by OwnerID or AccountID prevents multiple threads from attempting to lock the same parent records simultaneously. This approach ensures that the database index updates are serialized per specific record groups, drastically reducing the incidence of 'UNABLE_TO_LOCK_ROW' errors during large volume data loads in a multi-tenant environment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Perform the load using the standard Salesforce UI 'Import Wizard'.
Why it's wrong here
The standard Import Wizard is designed for small datasets and lacks the performance tuning capabilities required for 50 million records. It does not provide the necessary control over batch sizes or thread execution to manage contention effectively at this scale, leading to timeouts and significant process failures.
- ✗
Increase the batch size to the maximum allowed limit of 10,000.
Why it's wrong here
Increasing batch sizes to the absolute maximum often exacerbates locking contention. Large batches increase the duration of row locks, heightening the probability of other processes encountering locked records. Efficient large data loading typically requires smaller, optimized batch sizes to ensure individual transactions complete quickly without blocking other concurrent system activities.
- ✓
Sort the data by OwnerID and process records in serial mode.
Why this is correct
Sorting by OwnerID or a parent lookup field allows the system to process related records in a predictable order. By avoiding concurrent updates to the same parent or owner records, you reduce the likelihood of row-level lock contention, ensuring that the database engine can process the transaction blocks without waiting for locks.
- ✗
Disable all Validation Rules and Apex Triggers permanently.
Why it's wrong here
While disabling automation helps performance, permanently removing validation rules compromises data integrity. You should only disable these during the load window. Furthermore, simply disabling triggers does not address the fundamental database row-locking contention issues caused by multi-threaded processing of records associated with the same parent or owner.
About these practice questions
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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
This SF-Data-Arch practice question is part of Courseiva's free Salesforce certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the SF-Data-Arch exam.