Salesforce Data-Architect Exam Details & Actual Exam Questions
- Exam Code/Number: Data-Architect
- Exam Name/Title: Salesforce Certified Data Architect
- Certification Provider: Salesforce
- Corresponding Certification: Salesforce Application Architect
- Exam Questions: 260
- Updated On: Sep,07 2026
- Certification Level: Architect
Salesforce Certified Data Architect Exam Questions
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(43 Up Votes)Salesforce Data-Architect Exam Overview:
| Certification Vendor: | Salesforce |
|---|---|
| Exam Name: | Salesforce Certified Data Architect |
| Exam Number: | Data-Architect |
| Available Languages: | English |
| Real Exam Qty: | 60 |
| Exam Price: | USD 400 |
| Passing Score: | ~68% |
| Related Certifications: | Salesforce Certified System Architect Salesforce Certified Application Architect |
| Exam Duration: | 120 minutes |
| Exam Format: | Multiple-choice, Multiple-select |
| Sample Questions: | Salesforce Data-Architect Sample Questions |
| Exam Way: | Online proctored or onsite at a testing center |
| Pre Condition: | None |
| Official Syllabus URL: | https://www.salesforce.com/certifications/architect/data-architect/ |
Salesforce Data-Architect Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Governance | 20% | - Compliance and regulatory considerations - Data quality standards and metrics - Data stewardship roles and responsibilities - Data retention and archiving policies |
| Topic 2: Data Integration | 15% | - Sharing rules impact on data integration - Handling locks and parallelism during migration - Real-time vs batch integration patterns - ETL tools and data migration strategies - API-based integration (REST, SOAP, Bulk API) |
| Topic 3: Data Architecture | 20% | - Architecture patterns and design principles - Governor limits and bulk processing - Archiving and data lifecycle management - Performance tuning and optimization |
| Topic 4: Master Data Management | 20% | - Data cleansing techniques - Identity resolution - Data duplication and matching rules |
| Topic 5: Data Modeling | 25% | - Skinny tables for performance optimization - Indexing strategies - Normalization and denormalization strategies - Big Objects and external objects - Large Data Volumes (LDV) design considerations |
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