Salesforce Data-Architect Certification Exam is a challenging yet rewarding certification for professionals who specialize in Salesforce data architecture and management. Salesforce Certified Data Architect certification demonstrates a deep understanding of Salesforce data architecture concepts, best practices, and implementation strategies, which is highly sought after by employers. If you are looking to advance your career in Salesforce data architecture and management, earning the Salesforce Data-Architect Certification is an excellent way to do so.
Salesforce Data-Architect certification exam is designed to test the skills and knowledge required to design and implement data solutions on the Salesforce platform. Data-Architect exam covers various topics such as data modeling, data integration, data migration, data governance, and data security. Data-Architect exam is designed to test the candidate's ability to design and implement scalable, robust, and efficient data solutions on the Salesforce platform.
Salesforce Data-Architect certification exam consists of 60 multiple-choice questions, which are accompanied by a case study. You will be asked to apply your knowledge of Salesforce data architecture concepts and implement best practices in the scenario presented in the case study. Data-Architect exam is intended for professionals with advanced knowledge of data architecture concepts and experience designing complex Salesforce data models.
Reference: https://trailheadacademy.salesforce.com/certificate/exam-data-architect---Plat-Arch-201
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Salesforce Data-Architect Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Governance | 20% | - Compliance and regulatory considerations - Data retention and archiving policies - Data quality standards and metrics - Data stewardship roles and responsibilities |
| Topic 2: Data Modeling | 25% | - Large Data Volumes (LDV) design considerations - Big Objects and external objects - Skinny tables for performance optimization - Normalization and denormalization strategies - Indexing strategies |
| Topic 3: Master Data Management | 20% | - Data duplication and matching rules - Data cleansing techniques - Identity resolution |
| Topic 4: Data Architecture | 20% | - Governor limits and bulk processing - Archiving and data lifecycle management - Performance tuning and optimization - Architecture patterns and design principles |
| Topic 5: Data Integration | 15% | - Real-time vs batch integration patterns - Sharing rules impact on data integration - API-based integration (REST, SOAP, Bulk API) - Handling locks and parallelism during migration - ETL tools and data migration strategies |
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