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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Metadata Management | 8% | - Metadata repositories - Metadata lifecycle - Types of metadata |
| Topic 2: Reference and Master Data | 5% | - Management processes - Definitions and purpose |
| Topic 3: Data Quality | 12% | - Assessment and improvement - Data cleansing - Quality dimensions |
| Topic 4: Data Governance | 15% | - Governance framework and policies - Data stewardship - Compliance and ethics |
| Topic 5: Data Security | 10% | - Security principles - Access control - Privacy and protection |
| Topic 6: Data Storage and Operations | 8% | - Database operations - Storage technologies - Data lifecycle management |
| Topic 7: Data Architecture | 12% | - Architecture components and layers - Data modeling and design - Enterprise data architecture |
| Topic 8: Data Integration and Interoperability | 10% | - Integration patterns - ETL and ELT processes - Data exchange standards |
| Topic 9: Data Modeling and Design | 10% | - Modeling standards and methods - Conceptual, logical, physical models |
| Topic 10: Data Management Concepts | 10% | - Definitions and principles - Data management roles and responsibilities - DAMA-DMBOK framework |
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NEW QUESTION # 236
Data governance requires control mechanisms and procedures for, but not limited to, escalating issues to higher level of authority.
Answer: A
NEW QUESTION # 237
Two risks with the Matching process are:
Answer: A,B
NEW QUESTION # 238
Which of the following is NOT a goal of Data Quality?
Answer: E
NEW QUESTION # 239
GDPR and PIPEDA are examples of:
Answer: A
NEW QUESTION # 240
Data parsing is the process of analysing data using pre-determined rules to define its content or value.
Answer: A
NEW QUESTION # 241
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