SF-Data-Arch Salesforce Data Management Practice Question
A data architect is designing a data retention policy for a custom object Event_Log__c that stores 20 million records per year. The business requires that records older than two years be archived to an external system but remain accessible for audit purposes. What is the most appropriate approach to meet these requirements while minimizing storage costs?
⚠ Common exam trap
Watch out — candidates often confuse data visibility with data archival; hiding records via reports or field history does not remove them from storage.
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
✓
Implement a scheduled batch process that exports records older than two years to an external data warehouse and then deletes them from Salesforce.
A data retention policy requires physically moving data out of Salesforce to reduce storage and cost. A scheduled batch export followed by deletion is a standard pattern for archiving large volumes. It ensures records older than the retention period are removed from Salesforce while remaining available externally for audit. This approach is scalable and can be automated, aligning with data management best practices.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Implement a scheduled batch process that exports records older than two years to an external data warehouse and then deletes them from Salesforce.
Why this is correct
This approach aligns with a data retention policy: it moves old records to a cost-effective external system, deletes them from Salesforce to free storage, and maintains audit accessibility via the external system. A scheduled batch job can handle large volumes efficiently using the Bulk API or Batch Apex. It is the most appropriate way to meet the two-year retention requirement while minimizing storage costs.
- ✗
Enable field history tracking on Event_Log__c to retain old values for two years.
Why it's wrong here
Field history tracking retains changes to specific fields for up to 18-24 months depending on the edition, but it does not archive entire records. It also consumes storage and does not remove the original records. This option fails to provide an external archive and does not reduce storage costs from the 20 million records per year.
- ✗
Create a report filter to hide records older than two years from users.
Why it's wrong here
Filtering reports only hides records from view; it does not delete or archive them. The records still consume storage and count against data limits. This approach does not meet the requirement to archive to an external system, nor does it minimize storage costs. It is a cosmetic solution that leaves the data retention problem unsolved.
- ✗
Use Salesforce Big Objects to store all Event_Log__c records indefinitely.
Why it's wrong here
Big Objects are designed for massive data volumes and provide asynchronous, indexed access, but they are not intended for interactive real-time access. While they can reduce storage costs, they do not automatically archive records older than two years, and querying Big Objects requires Async SOQL or similar, which may not meet audit accessibility needs. This option does not address the retention policy requirement.
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.