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CDP
The ICCP Certified Data Professional (CDP) Foundation exam validates early‑career data specialists’ ability to apply Data Management Body of Knowledge concepts—covering fundamentals, ethics, governance, architecture, modeling, and storage—to real‑world challenges.
90
Minutes
Who Should Take This
Data analysts, junior data engineers, and emerging data stewards with six months to five years of experience should pursue this certification. They seek to solidify their grasp of DMBoK2 principles, demonstrate competency across all fourteen knowledge areas, and position themselves for advancement in data‑driven organizations.
What's Covered
1
Domain 1: Data Management Fundamentals and Ethics
2
Domain 2: Data Governance
3
Domain 3: Data Architecture
4
Domain 4: Data Modeling and Design
5
Domain 5: Data Storage and Operations
6
Domain 6: Data Security
7
Domain 7: Data Integration and Interoperability
8
Domain 8: Document and Content Management
9
Domain 9: Reference and Master Data
10
Domain 10: Data Warehousing and Business Intelligence
11
Domain 11: Metadata Management
12
Domain 12: Data Quality Management
13
Domain 13: Big Data and Data Science Fundamentals
What's Included in AccelaStudy® AI
Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
20 Activity Formats
Course Outline
69 learning goals
1
Domain 1: Data Management Fundamentals and Ethics
2 topics
Data management overview and framework
- Identify the fourteen knowledge areas of the DAMA Data Management Body of Knowledge and describe how Data Governance serves as the central organizing function connecting all other areas.
- Describe the DAMA functional framework including goals, principles, activities, roles, deliverables, and practices for each knowledge area of data management.
- Explain the environmental factors that influence data management including organizational culture, technology infrastructure, regulatory requirements, and data management maturity levels.
- Apply the DAMA data management maturity model to assess an organization's current capabilities and identify improvement priorities across knowledge areas.
Data handling ethics and responsibilities
- Identify ethical principles governing data handling including privacy, transparency, consent, data minimization, purpose limitation, and accountability.
- Describe the legal and regulatory landscape affecting data management including GDPR, CCPA, HIPAA, and sector-specific data protection requirements.
- Apply ethical data handling principles to evaluate data collection, usage, and retention scenarios and determine compliance with organizational data policies.
2
Domain 2: Data Governance
2 topics
Data governance framework and organization
- Identify the core components of a data governance framework including policies, standards, processes, roles, organizational structures, and metrics.
- Describe data governance roles including data owner, data steward, data custodian, data governance council, and chief data officer and their respective responsibilities.
- Apply data governance principles to design policies for data access, data sharing, and data lifecycle management within an organizational context.
Data stewardship and compliance
- Describe the data stewardship function including business data stewards, technical data stewards, coordinating stewards, and stewardship operating models.
- Identify compliance requirements that drive data governance activities including regulatory mandates, industry standards, contractual obligations, and internal policies.
- Analyze a data governance maturity scenario to identify gaps in stewardship coverage, policy enforcement, and compliance monitoring.
3
Domain 3: Data Architecture
2 topics
Enterprise data architecture concepts
- Identify the components of enterprise data architecture including data models, data flow diagrams, integration patterns, technology architecture, and data standards.
- Describe data architecture frameworks and their relationship to enterprise architecture including alignment with business strategy and technology roadmaps.
- Apply data architecture principles to evaluate data flow patterns and recommend integration approaches for a given business scenario.
Data lifecycle and integration architecture
- Describe the data lifecycle from creation through archival and disposal including data lineage tracking, provenance, and impact analysis concepts.
- Identify common data integration architecture patterns including hub-and-spoke, point-to-point, publish-subscribe, and data virtualization approaches.
- Analyze a data architecture scenario to identify integration bottlenecks, redundant data stores, and misalignment between data flows and business requirements.
4
Domain 4: Data Modeling and Design
2 topics
Data modeling fundamentals
- Identify the three levels of data modeling including conceptual, logical, and physical models and describe their purposes, audiences, and typical notation systems.
- Describe entity-relationship modeling concepts including entities, attributes, relationships, cardinality, optionality, and normalization forms through third normal form.
- Apply data modeling techniques to create a logical data model from business requirements, defining entities, attributes, primary keys, and relationships.
Dimensional and NoSQL modeling
- Describe dimensional modeling concepts including fact tables, dimension tables, star schemas, snowflake schemas, slowly changing dimensions, and conformed dimensions.
- Identify NoSQL data modeling approaches including key-value, document, column-family, and graph models and describe when each is appropriate.
- Analyze a business analytics scenario to determine whether a relational, dimensional, or NoSQL modeling approach best satisfies the data access and performance requirements.
5
Domain 5: Data Storage and Operations
2 topics
Database management and storage technologies
- Identify database management system types including relational, columnar, in-memory, graph, document, and time-series databases and their appropriate use cases.
- Describe database storage concepts including tablespaces, indexing strategies, partitioning, compression, and storage area network configurations.
- Apply database administration practices to configure backup schedules, recovery procedures, performance monitoring, and capacity planning for production databases.
Data operations and availability
- Describe data operations management including change management, release management, incident management, and service level agreements for data services.
- Identify high availability and disaster recovery strategies for data platforms including replication, clustering, failover, and business continuity planning.
- Analyze a data operations scenario to recommend appropriate storage technologies, availability configurations, and recovery strategies for given SLA requirements.
6
Domain 6: Data Security
2 topics
Data security policies and access control
- Identify data security requirements including confidentiality, integrity, availability, authentication, authorization, and audit controls as applied to data assets.
- Describe access control mechanisms for data including role-based access control, attribute-based access, row-level security, column-level masking, and dynamic data masking.
- Apply data security controls to implement encryption at rest and in transit, configure access control lists, and establish audit logging for sensitive data assets.
Data privacy and classification
- Describe data classification schemes including public, internal, confidential, and restricted categories and their impact on handling, storage, and access requirements.
- Apply data privacy techniques including anonymization, pseudonymization, tokenization, and data minimization to protect personally identifiable information.
- Analyze a data security scenario to identify vulnerabilities in access controls, classification gaps, and privacy compliance risks requiring remediation.
7
Domain 7: Data Integration and Interoperability
2 topics
Data integration processes and techniques
- Identify data integration approaches including ETL, ELT, data virtualization, change data capture, message queuing, and API-based integration patterns.
- Describe data transformation techniques including data cleansing, standardization, deduplication, format conversion, and aggregation during integration processes.
- Apply ETL design patterns to build data pipelines that extract from heterogeneous sources, transform according to business rules, and load into target data stores.
Data interoperability and standards
- Describe data interoperability standards and formats including XML, JSON, CSV, Parquet, Avro, and protocol specifications that enable data exchange between systems.
- Apply data integration techniques to resolve data conflicts, reconcile schema differences, and ensure semantic consistency across source systems.
8
Domain 8: Document and Content Management
1 topic
Content management and document control
- Identify document and content management system components including repositories, version control, workflow automation, taxonomy, and records retention schedules.
- Describe unstructured data management challenges including content indexing, search optimization, digital asset management, and electronic records management.
- Apply content management practices to design a document lifecycle including creation, review, approval, publication, archival, and disposition workflows.
9
Domain 9: Reference and Master Data
1 topic
Reference and master data management
- Identify the difference between reference data and master data and describe how each supports data consistency, reporting accuracy, and cross-system interoperability.
- Describe master data management implementation styles including registry, consolidation, coexistence, and centralized approaches with their trade-offs.
- Apply reference data management practices to establish code value hierarchies, cross-reference mappings, and versioning controls for enterprise reference datasets.
- Analyze a master data quality scenario to identify duplicate records, inconsistent golden records, and governance gaps requiring stewardship intervention.
10
Domain 10: Data Warehousing and Business Intelligence
2 topics
Data warehousing concepts and architecture
- Identify data warehouse architecture components including staging areas, operational data stores, enterprise data warehouses, data marts, and data lakehouse patterns.
- Describe data warehouse design methodologies including Inmon top-down, Kimball bottom-up, and hybrid approaches and their impact on enterprise analytics.
- Apply data warehouse design principles to create a dimensional model with appropriate fact and dimension tables for a given business reporting scenario.
Business intelligence and reporting
- Describe business intelligence capabilities including OLAP cubes, dashboards, scorecards, ad-hoc query tools, and self-service analytics platforms.
- Apply BI tool configuration to create reports, visualizations, and interactive dashboards that communicate business metrics to stakeholders effectively.
11
Domain 11: Metadata Management
1 topic
Metadata types and management
- Identify metadata categories including business metadata, technical metadata, operational metadata, and process metadata and describe their role in data management.
- Describe metadata management activities including metadata repository design, metadata harvesting, data lineage tracking, and business glossary maintenance.
- Apply metadata management practices to build a data catalog with business definitions, technical lineage, and usage metrics that support data discovery and governance.
12
Domain 12: Data Quality Management
2 topics
Data quality dimensions and assessment
- Identify data quality dimensions including accuracy, completeness, consistency, timeliness, uniqueness, and validity and describe measurement approaches for each.
- Describe data profiling techniques including column analysis, cross-column analysis, cross-table analysis, and data pattern discovery for quality assessment.
- Apply data quality rules and validation logic to implement automated data quality checks within data pipelines and integration processes.
Data quality improvement and monitoring
- Describe data quality improvement methodologies including root cause analysis, data cleansing strategies, and continuous monitoring through data quality dashboards and scorecards.
- Apply data quality management processes to design a data quality improvement plan with defined metrics, thresholds, escalation procedures, and remediation workflows.
- Analyze data quality assessment results to prioritize remediation efforts based on business impact, data criticality, and cost-benefit analysis.
13
Domain 13: Big Data and Data Science Fundamentals
2 topics
Big data concepts and technologies
- Identify big data characteristics including volume, velocity, variety, veracity, and value and describe how they differentiate big data from traditional data management.
- Describe big data technologies including Hadoop, Spark, data lakes, stream processing engines, and distributed storage systems and their roles in big data architectures.
- Apply big data concepts to evaluate when distributed processing frameworks are appropriate versus traditional database solutions for a given data volume and velocity scenario.
Data science and analytics foundations
- Describe the data science lifecycle including problem definition, data collection, exploratory data analysis, feature engineering, modeling, evaluation, and deployment.
- Identify foundational analytics techniques including descriptive statistics, predictive analytics, prescriptive analytics, and machine learning categories at a conceptual level.
- Analyze a business scenario to recommend the appropriate analytics approach, distinguishing between descriptive reporting, predictive modeling, and prescriptive optimization.
Scope
Included Topics
- All fourteen knowledge areas of the DAMA Data Management Body of Knowledge (DMBoK2) as tested on the ICCP Certified Data Professional Foundation exam: Data Management Overview, Data Handling Ethics, Data Governance, Data Architecture, Data Modeling and Design, Data Storage and Operations, Data Security, Data Integration and Interoperability, Document and Content Management, Reference and Master Data, Data Warehousing and Business Intelligence, Metadata Management, Data Quality Management, and Big Data and Data Science.
- Foundational concepts of data management including the DAMA functional framework, environmental factors influencing data management, data management maturity models, organizational structures, and the data management lifecycle.
- Core terminology and principles across all DMBoK2 knowledge areas sufficient for the Foundation-level exam requiring a minimum 60% passing score on 100 scored questions.
- Data management process fundamentals, data handling ethics, and the role of data governance as the central organizing discipline connecting all other knowledge areas.
Not Covered
- Deep implementation-level expertise in any single DMBoK2 knowledge area beyond what Foundation-level candidates are expected to know.
- Vendor-specific product configurations or platform-specific administration tasks not referenced in the DMBoK2 body of knowledge.
- Advanced statistical modeling, machine learning algorithms, or data science programming techniques covered by the CDS certification.
- Enterprise architecture frameworks (TOGAF, Zachman) beyond their relationship to data architecture principles.
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