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Salesforce Certified Data Architect certification exam is a comprehensive test that covers a wide range of topics including data modeling, data integration, data governance, data security, and data management. Data-Architect exam is designed to assess the candidate's ability to design and implement complex data solutions in the Salesforce platform. Data-Architect exam consists of multiple-choice questions and performance-based scenarios that test the candidate's knowledge and skills.
Salesforce Data-Architect certification is the highest level of certification available for individuals who specialize in Salesforce data management. Salesforce Certified Data Architect certification requires a deep understanding of data architecture principles, data modeling, and data integration. Candidates who pass this certification exam are considered experts in designing and managing data architectures on the Salesforce platform. Salesforce Certified Data Architect certification is ideal for individuals who want to take their Salesforce career to the next level and become experts in data management.
Reference: https://trailheadacademy.salesforce.com/certificate/exam-data-architect---Plat-Arch-201
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Integration | 15% | - API-based integration (REST, SOAP, Bulk API) - Handling locks and parallelism during migration - Sharing rules impact on data integration - Real-time vs batch integration patterns - ETL tools and data migration strategies |
| Topic 2: Data Architecture | 20% | - Performance tuning and optimization - Archiving and data lifecycle management - Architecture patterns and design principles - Governor limits and bulk processing |
| Topic 3: Data Governance | 20% | - Data stewardship roles and responsibilities - Data retention and archiving policies - Compliance and regulatory considerations - Data quality standards and metrics |
| Topic 4: Master Data Management | 20% | - Data duplication and matching rules - Identity resolution - Data cleansing techniques |
| Topic 5: Data Modeling | 25% | - Indexing strategies - Big Objects and external objects - Normalization and denormalization strategies - Large Data Volumes (LDV) design considerations - Skinny tables for performance optimization |
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