Salesforce Certified Data Architecture Practice Test

What approach should an Architect recommend to enforce a Data Quality Plan throughout an organization?

1. Ensure all data is stored in an external system and set up an integration to Salesforce.

2. Schedule reports that will automatically catch duplicates and merge or delete records weekly.

3. Enforce critical business processes using Workflow, Validation Rules, and Apex code.

4. Schedule a weekly dashboard displaying records missing information for management review.

The correct answer is: Enforce critical business processes using Workflow, Validation Rules, and Apex code.

The recommendation to enforce a Data Quality Plan through the enforcement of critical business processes using Workflow, Validation Rules, and Apex code is essential for maintaining data integrity throughout an organization. This approach allows an architect to create systematic checks and balances that ensure only data meeting specific criteria is allowed into the system. Workflow rules can automate standard processes and ensure that certain actions, such as sending notifications or updating fields, occur automatically based on predefined conditions. Validation rules act as safeguard measures by validating data on input to guarantee compliance with defined business rules, preventing incorrect or incomplete data from being saved in the system. Apex code can be employed to implement complex business logic that might exceed the capabilities of standard declarative tools, allowing for more sophisticated data validations and processes. This holistic approach is effective because it not only addresses data quality at the point of entry but also creates an ongoing mechanism for data governance, adjusting to evolving business needs and ensuring that data quality is prioritized across the organization. By embedding these processes directly into the system's architecture, an organization can promote a culture of data quality that is sustained over time. Other options, while they may contribute to data quality efforts, lack the comprehensive enforcement mechanisms necessary for long-term effectiveness. For instance, relying only on scheduled reports may not proactively