| Exam Code | Certified-Data-Architecture-and-Management-Designer |
| Questions and Answers | 144 |
| Premium Access | Yes |
| Online Test Engine | Yes |
| Comprehensive Q&A | Yes |
| Success Rate | 98% |
| Real Questions | Yes |
| Updated Regularly | Yes |
| Portable Files | ✔ |
| Unlimited Download | ✔ |
| 100% Secured | ✔ |
| Confidentiality | 100% |
| Success Guarantee | 100% |
| Any Hidden Cost | $0.00 |
| Auto Recharge | No |
| Updates Intimation | by Email |
| Technical Support | Free |
| PDF Compatibility | Windows, Android, iOS, Linux |
| Test Engine Compatibility | Mac/Windows/Android/iOS/Linux |
| Sample Questions |  |
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Exam Details for Certified Data Architecture and Management Designer:
Number of Questions: The exam consists of approximately 60 multiple-choice and multiple-select questions.
Time Limit: The total time allocated for the exam is 105 minutes (1 hour and 45 minutes).
Passing Score: To pass the exam, you must achieve a minimum score of 65%.
Exam Format: The exam is conducted online and is proctored. You will be required to answer the questions within the allocated time frame.
Course Outline:
1. Data Architecture Management:
- Understand data architecture concepts and principles
- Define and implement data governance strategies
- Establish data quality and data lifecycle management processes
2. Data Modeling and Design:
- Identify business requirements and translate them into data models
- Design logical and physical data models
- Apply normalization and denormalization techniques
3. Data Integration and Transformation:
- Design and implement data integration solutions
- Manage data transformation processes
- Utilize ETL (Extract, Transform, Load) tools and techniques
4. Data Security and Compliance:
- Implement data security controls and access restrictions
- Ensure compliance with data protection regulations (e.g., GDPR, CCPA)
- Monitor and mitigate data security risks
5. Data Storage and Infrastructure:
- Select appropriate data storage solutions (e.g., relational databases, data warehouses, big data platforms)
- Optimize data storage and retrieval performance
- Design and implement scalable and resilient data infrastructure
6. Data Migration and Synchronization:
- Plan and execute data migration strategies
- Ensure data consistency and integrity during migration
- Synchronize data between different systems and environments
7. Data Governance and Metadata Management:
- Define and implement data governance frameworks
- Establish metadata management processes
- Utilize data cataloging and data lineage tools
Exam Objectives:
1. Understand data architecture concepts, principles, and best practices.
2. Design and implement data models to meet business requirements.
3. Develop data integration and transformation solutions.
4. Ensure data security and compliance in data management processes.
5. Select and optimize data storage solutions.
6. Plan and execute data migration and synchronization strategies.
7. Establish data governance and metadata management frameworks.
Exam Syllabus:
The exam syllabus covers the following topics:
1. Data Architecture Management
- Data architecture concepts and principles
- Data governance strategies
- Data quality and lifecycle management
2. Data Modeling and Design
- Business requirements analysis
- Logical and physical data models
- Normalization and denormalization techniques
3. Data Integration and Transformation
- Data integration solutions
- Data transformation processes
- ETL tools and techniques
4. Data Security and Compliance
- Data security controls and access restrictions
- Data protection regulations (e.g., GDPR, CCPA)
- Data security risk management
5. Data Storage and Infrastructure
- Data storage solutions selection
- Performance optimization techniques
- Scalable and resilient data infrastructure design
6. Data Migration and Synchronization
- Data migration strategies
- Data consistency and integrity during migration
- Data synchronization between systems
7. Data Governance and Metadata Management
- Data governance frameworks
- Metadata management processes
- Data cataloging and data lineage tools
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