Master Data Management Practice Exam
Master Data Management Practice Exam
About Master Data Management Exam
The Master Data Management Practice Exam is designed to test your ability to plan, implement and maintain master data processes and systems. This exam measures your skills in data governance, modelling, integration, quality control and the use of MDM platforms. Whether you work as a data analyst, data manager or IT professional, this exam will help you find your strengths and show where you need to improve.
Who should take the Exam?
- Data analysts and data stewards
- Master data managers and architects
- IT professionals involved in data integration
- Business analysts working with data governance
- Consultants and project managers in data projects
- Anyone responsible for managing key business data
Skills Required
- Basic understanding of data management principles
- Familiarity with data governance concepts
- Ability to model and organise data entities
- Comfort with ETL and integration workflows
- Attention to detail for data quality checks
Knowledge Gained
- How to set up a master data governance framework
- Techniques for modelling and organising master data
- Methods to integrate data from multiple sources
- Steps to profile, cleanse and monitor data quality
- Best practices for configuring and using MDM tools
- Ways to maintain and scale MDM operations
- Insights from real-world MDM implementations
Course Outline
The Master Data Exam covers the following topics -
Domain 1 – Fundamentals of Master Data Management
- Definition and scope of master data
- Benefits and challenges of MDM
- Key components: governance, stewardship, quality
Domain 2 – Data Governance and Stewardship
- Building a governance framework
- Defining roles and responsibilities
- Data stewardship processes and policies
Domain 3 – Data Modelling for MDM
- Entity relationship modelling
- Hierarchies and data domains
- Reference versus transactional data
Domain 4 – Data Integration and ETL
- Extract, transform, load processes
- Integrating data from disparate sources
- Real-time and batch integration methods
Domain 5 – Data Quality Management
- Profiling and assessing data quality
- Cleansing and standardisation techniques
- Monitoring and reporting quality metrics
Domain 6 – MDM Implementation and Tools
- Overview of MDM architectures
- Selecting and configuring MDM platforms
- Implementation lifecycle and best practices
Domain 7 – MDM Operations and Maintenance
- Version control and change management
- Issue resolution and exception handling
- Performance tuning and scalability
Domain 8 – Advanced Topics and Case Studies
- Multi-domain and cross-domain MDM
- Reference data management
- Real-world case studies and lessons learned
