SF-Data-Arch Data Governance Practice Question
A Salesforce org is implementing a data governance program to manage data quality for a large volume of customer records. The architect must recommend tools and features to monitor and improve data quality on an ongoing basis. Which two Salesforce features should be used to proactively identify and address data quality issues? (Choose two.)
⚠ Common exam trap
The trap here is assuming that reporting tools or data loading tools proactively enforce data quality, when they are passive or manual.
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
✓
Duplicate Management with Matching Rules and Duplicate Rules
Duplicate Management and Validation Rules are proactive features that identify and prevent data quality issues. Duplicate Management detects and manages duplicate records, while Validation Rules enforce data quality at entry by blocking invalid saves. Data Loader, Salesforce Optimizer, and Reports/Dashboards are not proactive data quality tools; they serve other purposes like data loading, org optimization, or monitoring.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Duplicate Management with Matching Rules and Duplicate Rules
Why this is correct
Duplicate Management allows you to define matching rules to identify duplicate records and duplicate rules to control what happens when duplicates are detected (e.g., alert, block, or allow). This proactively identifies and prevents duplicate data entry, which is a key aspect of data quality. It can be configured for standard and custom objects, and runs in real-time on record creation and edit, as well as via batch jobs for existing data.
- ✗
Data Loader with batch size optimization
Why it's wrong here
Data Loader is a tool for bulk data import and export, not for ongoing data quality monitoring. While batch size optimization can improve performance, it does not proactively identify or address data quality issues. It is a manual tool used for data migration or periodic loads, and does not provide real-time or scheduled data quality checks. It is not a governance feature for continuous data quality management.
- ✗
Salesforce Optimizer
Why it's wrong here
Salesforce Optimizer analyzes your org's configuration and provides recommendations for improving performance and security, but it does not focus on data quality. It looks at things like unused fields, report performance, and user permissions. It does not identify duplicate records, incomplete data, or other data quality issues. Therefore, it is not the right tool for ongoing data quality monitoring.
- ✓
Validation Rules to enforce data quality at entry
Why this is correct
Validation Rules enforce data quality by preventing users from saving records that do not meet specified criteria, such as missing required fields or invalid formats. They run on record creation and edit, providing proactive data quality enforcement. They can be used to ensure data completeness, consistency, and accuracy. This is a fundamental Salesforce feature for maintaining data quality at the point of entry.
- ✗
Reports and Dashboards with custom report types
Why it's wrong here
Reports and Dashboards can be used to visualize data quality metrics, such as the number of records missing a required field or with invalid values. However, they are passive tools that require manual review and do not proactively identify or address issues. They can complement a data quality program but are not proactive enforcement or detection features like Duplicate Management. They are more for monitoring and analysis.
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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