2026 New Data-Architect Exam Questions Real Salesforce Dumps
Course 2026 Data-Architect Test Prep Training Practice Exam Download
To become a Salesforce Certified Data Architect, applicants must demonstrate proficiency in several areas, including data modeling, data governance, data integration, data migration, data security, and data quality. Data-Architect exam is designed to test the applicant’s knowledge of these topics, as well as their ability to apply their knowledge to real-world scenarios. Salesforce Certified Data Architect certification program is recognized globally and is highly valued by employers who are looking for experts in data architecture. Additionally, the certification program is constantly evolving to keep up with changes in the Salesforce platform, ensuring that certified professionals stay up-to-date with the latest trends and technologies in data architecture.
Salesforce Data-Architect Exam Syllabus Topics:
| Topic |
Details |
| Topic 1 |
- Data Migration: This topic covers suitable techniques and methods to make sure high data quality at load time, different strategies to improve performance when migrating large data volumes into Salesforce, and various techniques and considerations to export data from Salesforce.
|
| Topic 2 |
- Master Data Management: This topic covers the various techniques, approaches, and considerations to implement Master Data Management Solutions, as well as techniques for establishing a “golden record” or “system of truth” for the customer domain. It also discusses approaches and techniques for consolidating data attributes and maintaining customer reference and metadata.
|
| Topic 3 |
- Data modeling
- Database Design: This topic covers various techniques and considerations for designing a data model for the Customer 360 platform. It also focuses on approaches to designing a scalable data model that obeys the current security and sharing model. The topic also compares and contrasts techniques for capturing and managing business and technical metadata, as well as the reasons for implementing Big Objects vs Standard
- Custom objects and approaches to avoid data skew.
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| Topic 4 |
- Large Data Volume Considerations: This covers the design of a data model that scales considering large data volume and solution performance, a data archiving and purging plan, and the use of virtualized data options.
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