CDMP-Associate Exam Information and Outline
Certified Data Management Professional - Associate
CDMP-Associate Exam Syllabus & Study Guide
Before you start practicing with our exam simulator, it is essential to understand the
official CDMP-Associate exam objectives. This course outline serves as your roadmap.
The information below reflects the 2026 syllabus defined by
DAMA.
Below are complete topics detail with latest syllabus and course outline, that will help you good knowledge about exam objectives and topics that you have to prepare. These contents are covered in questions and answers pool of exam.
DAMA CDMP-Associate Certified Data Management Professional - Associate
High-Weight Core Domains (44% Total)
Chapter 5: Data Modeling and Design (11%)
- Conceptual, logical, and physical data models
- Entity-Relationship (ER) modeling, normalization, and dimensional design principles
- Data model patterns and integration with metadata
Chapter 12: Metadata Management (11%)
- Categories of metadata (business, technical, operational)
- Metadata architecture, repositories, and integration standards
- Governance and quality control processes for metadata
Chapter 13: Dimensions of Data Quality (11%)
- Core data quality dimensions (accuracy, completeness, consistency, timeliness, validity, uniqueness)
- Data quality management lifecycle (assess, measure, analyze, improve)
- Data profiling, issue tracking, and root-cause analysis
Chapter 3: Data Governance (11%)
- Data governance frameworks, operating models, and strategy
- Core roles and responsibilities (Data Owners, Data Stewards, Data Governance Boards)
- Policy enforcement, issue management, and organizational change management
Intermediate-Weight Domains (36% Total)
Chapter 10: Reference and Master Data Management (10%)
- Differences between reference data and master data
- Matching, merging, survivorship rules, and golden record creation
- Master Data Management (MDM) architectures and styles
Chapter 11: Data Warehousing and Business Intelligence (10%)
- Data warehousing architectures (Inmon vs. Kimball methodologies)
- Dimensional modeling (star schemas, snowflake schemas, and data marts)
- ETL/ELT strategies, operational data stores (ODS), and reporting layers
Chapter 4: Data Architecture (6%)
- Enterprise architecture frameworks and data value chains
- Data architecture deliverables, subject area models, and enterprise data flows
Chapter 6: Data Storage and Operations (6%)
- Database administration, performance monitoring, backup, and recovery
- Storage lifecycle, capacity planning, and technology variations (relational, NoSQL, in-memory)
Chapter 7: Data Security (6%)
- Data security lifecycles, classification, and threat risk management
- Access controls, masking, encryption, auditing, and compliance with data privacy regulations (e.g., GDPR)
Chapter 8: Data Integration and Interoperability (6%)
- Integration mechanisms (ETL, enterprise service buses, APIs, data virtualization)
- Data movement, transformation patterns, federation, and replication
Chapter 9: Document and Content Management (6%)
- Managing unstructured data and electronic records management (ERM)
- Taxonomies, classification schemas, document lifecycle, and retention policies
Foundational and Contextual Domains (20% Total)
Chapter 1: Data Management (2%)
- Definition of data management and the DAMA Wheel framework
- Data lifecycle management and treating data as an enterprise asset
Chapter 2: Data Handling Ethics (2%)
- Ethical frameworks, privacy by design, and moral responsibilities
- Transparency, consent, compliance, and protection of individual rights
Chapter 14: Big Data and Data Science (2%)
- Characteristics of Big Data (Volume, Velocity, Variety, Veracity, Value)
- Big data architectures (data lakes, Hadoop ecosystems) and machine learning fundamentals