SF-Data-Arch Salesforce Data Management Practice Question
A Data Architect is designing a data governance strategy for a Salesforce org that integrates with multiple external systems. The architect needs to ensure data quality and consistency across systems. Which two practices should be implemented? (Choose two.)
⚠ Common exam trap
The trap here is thinking that manual reconciliation or data exports are sufficient for data governance, but they do not prevent data quality issues at the source.
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
✓
Define a master data management (MDM) strategy to establish a single source of truth.
The two correct practices are defining an MDM strategy and implementing validation and duplicate rules. MDM establishes a single source of truth, while validation and duplicate rules enforce data quality at the point of entry. Together, they ensure data consistency and accuracy across integrated systems, which is essential for effective data governance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Schedule nightly full data exports to a data warehouse for backup purposes.
Why it's wrong here
Full data exports are useful for backup and analytics but do not improve data quality or consistency in the source system. They are a downstream activity. Data governance should focus on preventing issues at the source, not just copying data elsewhere. This practice does not enforce data quality rules or synchronization across systems.
- ✗
Configure field-level security to restrict access to sensitive fields.
Why it's wrong here
Field-level security controls access to fields but does not address data quality or consistency. It is a security measure, not a data governance practice for ensuring accuracy. While important, it does not prevent duplicates or invalid data from being created. It is not directly related to the scenario's goal of data quality and consistency.
- ✓
Define a master data management (MDM) strategy to establish a single source of truth.
Why this is correct
An MDM strategy ensures that critical data entities have a single, authoritative source. It defines data ownership, stewardship, and synchronization rules. This reduces data duplication and inconsistency across systems. By implementing MDM, the architect can enforce data quality and consistency, which is essential for integrated environments.
- ✗
Use Salesforce Data Loader to manually reconcile data discrepancies on a weekly basis.
Why it's wrong here
Manual reconciliation with Data Loader is error-prone and not scalable. It does not provide a systematic approach to data governance. While it can be a temporary fix, it is not a best practice for ensuring ongoing data quality and consistency across multiple systems. It also lacks automation and auditability.
- ✓
Implement validation rules and duplicate rules to enforce data quality at the point of entry.
Why this is correct
Validation rules and duplicate rules help enforce data quality by preventing invalid or duplicate records from being saved. They provide immediate feedback to users and can be applied to data loaded via APIs. This ensures that data entering Salesforce meets predefined standards, which is a key aspect of data governance.
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
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