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Salesforce Data-Architect certification exam is intended for professionals who have experience in Salesforce data architecture and management. Candidates should have a deep understanding of Salesforce data model, data architecture, and data management principles. They should also have experience in designing and implementing data integration, migration, and analytics solutions in Salesforce.
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Salesforce Certified Data Architect exam covers a wide range of topics, including data modeling, data migration, data integration, data analysis, and data governance. It is designed to test the candidate's ability to design and implement complex data architectures that meet the needs of a business. Data-Architect-JPN exam consists of multiple-choice questions and scenario-based questions that require the candidate to analyze a given situation and provide the best possible solution.
Reference: https://trailheadacademy.salesforce.com/certificate/exam-data-architect---Plat-Arch-201
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To become a Salesforce Certified Data Architect, candidates must pass the Data-Architect certification exam. Data-Architect-JPN exam is designed to test candidates on their ability to design and implement complex data models, integrate data from multiple sources, and ensure data quality and security. Candidates must have a deep understanding of Salesforce data architecture and data management best practices to pass Data-Architect-JPN exam.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Master Data Management | 20% | - Data cleansing techniques - Data duplication and matching rules - Identity resolution |
| Topic 2: Data Architecture | 20% | - Governor limits and bulk processing - Architecture patterns and design principles - Performance tuning and optimization - Archiving and data lifecycle management |
| Topic 3: Data Integration | 15% | - ETL tools and data migration strategies - API-based integration (REST, SOAP, Bulk API) - Handling locks and parallelism during migration - Real-time vs batch integration patterns - Sharing rules impact on data integration |
| Topic 4: Data Modeling | 25% | - Normalization and denormalization strategies - Skinny tables for performance optimization - Large Data Volumes (LDV) design considerations - Big Objects and external objects - Indexing strategies |
| Topic 5: Data Governance | 20% | - Data stewardship roles and responsibilities - Data quality standards and metrics - Compliance and regulatory considerations - Data retention and archiving policies |
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