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Salesforce Data-Architect certification is a highly sought-after credential in the IT industry, particularly for professionals who work with Salesforce. Salesforce Certified Data Architect (Data-Architect日本語版) certification demonstrates a professional's proficiency in designing and implementing data solutions that meet the complex needs of modern businesses. Salesforce Certified Data Architect (Data-Architect日本語版) certification exam covers a wide range of topics related to Salesforce data architecture, including data modeling, data integration, data migration, and data governance. It is an advanced certification that requires a deep understanding of Salesforce's data architecture and best practices for managing complex data solutions. Achieving the Salesforce Data-Architect certification can help professionals advance their careers and increase their earning potential.

Reference: https://trailheadacademy.salesforce.com/certificate/exam-data-architect---Plat-Arch-201

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Salesforce Data-Architect日本語 Exam Syllabus Topics:

SectionWeightObjectives
Data Modeling and Database Design25%- Indexing and data structure
  • 1. Standard and custom indexes
  • 2. Skinny tables and big objects
  • 3. Query optimization strategies
- Design scalable data models
  • 1. Object relationships and cardinality
  • 2. Data model optimization for performance
  • 3. Custom and standard object design
Data Governance and Compliance20%- Data governance framework
  • 1. Compliance and regulatory requirements
  • 2. Data quality standards
  • 3. Data stewardship and ownership
- Data lifecycle management
  • 1. Data classification and handling
  • 2. Data retention and archiving policies
  • 3. Data privacy and security
Salesforce Data Management25%- Sharing and visibility design
  • 1. Data access and security model
  • 2. Organization-wide defaults, role hierarchy
  • 3. Sharing rules, manual sharing, Apex sharing
- Data import and export strategies
  • 1. Migration planning and execution
  • 2. Tools: Data Loader, Import Wizard, Bulk API
  • 3. Data validation and cleansing
Large Data Volume Considerations20%- Storage and optimization
  • 1. Archiving and off-platform storage
  • 2. Storage limits and management
  • 3. Performance tuning techniques
- Performance and scalability
  • 1. Governor limits and bulk processing
  • 2. Handling millions of records
  • 3. Avoiding and resolving data skew
Data Integration and Master Data Management10%- Integration patterns and tools
  • 1. External data sources and objects
  • 2. REST, SOAP, and Bulk API usage
  • 3. Real-time vs batch integration
- Master data management
  • 1. Single source of truth design
  • 2. MDM strategy and implementation
  • 3. Data synchronization and consistency

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