SF-Data-Arch Large Data Volume Considerations Practice Question
A company is migrating 100 million records into Salesforce. Which TWO actions should the architect take to optimize performance and prevent row locking?
⚠ Common exam trap
Candidates often overlook the impact of indexes on DML performance, assuming more indexes are always better, when in reality, every index adds overhead during mass record inserts.
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
✓
Disable unnecessary triggers during the data load.
High-volume data loads require careful management of record locking and indexing. Disabling triggers during the load prevents unnecessary processing and lock contention. Furthermore, reducing the number of indexes on the target object minimizes the overhead required for every insert operation, as Salesforce must update every index on the object for each new record processed during the migration process.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable all custom indexes before the data load.
Why it's wrong here
Enabling custom indexes before a bulk load significantly slows down the insertion process. The system must update every index for every single record inserted, causing a performance degradation and increasing the risk of transaction timeouts or database lock contention during the high-volume data ingestion phase.
- ✓
Disable unnecessary triggers during the data load.
Why this is correct
Triggers run for every record processed, consuming CPU and database resources while creating row locks on parent or related objects. Disabling them allows the system to focus exclusively on record insertion, significantly increasing throughput and avoiding potential deadlocks caused by concurrent logic execution during the migration.
- ✗
Use the Bulk API 2.0 with serial mode.
Why it's wrong here
Serial mode processes batches one at a time, which is significantly slower than parallel mode. While it might prevent some locking, it is rarely the optimal approach for 100 million records. Parallel mode is preferred for performance, provided the data is organized to avoid lock contention.
- ✓
Reduce the number of indexes on the target object.
Why this is correct
Every index requires an update during the record insertion process. By minimizing the number of indexes, the database engine spends less time updating metadata and more time writing the actual data. This reduction is a critical best practice for accelerating high-volume data loads into Salesforce objects.
- ✗
Increase the batch size to 10,000.
Why it's wrong here
Salesforce imposes strict batch size limits, typically up to 2,000 for standard bulk processing. Attempting to use 10,000 would result in an error or performance issues, as larger batches increase the likelihood of transaction size limit violations and prolonged row locking across related tables.
About these practice questions
Courseiva writes every SF-Data-Arch question from scratch — 222 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
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.