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CDMP Cheat Sheet

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Data Governance

  • 11% of exam
  • Stewardship Governance Council Operating Models DAMA Definitions

Data Modeling and Design

  • 11% of exam
  • Model Levels Normalization Notations Naming Standards

Data Quality

  • 11% of exam
  • DQ Dimensions DMAIC Root Cause Profiling

Metadata Management

  • 11% of exam
  • Metadata Types Lineage Repository Architecture CWM

Master and Reference Data

  • 10% of exam
  • MDM Architectures Golden Record Match-Merge Reference Data

Data Warehousing and BI

  • 10% of exam
  • Inmon vs Kimball Star Schema SCD Types OLAP

Data Architecture

  • 6% of exam
  • Zachman Framework Enterprise Model Data Flow TOGAF Alignment

Document and Content Management

  • 6% of exam
  • ECM Retention e-Discovery GARP

Data Integration and Interoperability

  • 6% of exam
  • ETL vs ELT CDC ESB Canonical Model

Data Security

  • 6% of exam
  • CIA Triad RBAC Encryption Privacy Law

Data Storage and Operations

  • 6% of exam
  • DBA Role NoSQL Backup Recovery Performance Tuning

Big Data and Data Science

  • 2% of exam
  • Four Vs Data Lake Hadoop Spark Analytics Maturity

Data Ethics

  • 2% of exam
  • Bias Fairness Transparency Belmont Principles

Data Management Process and Maturity

  • 2% of exam
  • Environmental Hexagon CMMI-DMM DCAM Maturity Assessment

Quick Facts

  • Exam CDMP Fundamentals
  • Body DAMA International
  • Questions 100
  • Time 90 min (110 ESL)
  • Pass 60/70/80%
  • Format Open-book, online-proctored
  • Level Associate/Practitioner/Master
  • Blueprint DMBoK2 Revised (Mar 2024)

Steward vs Custodian DA

Data Steward

  • Owns business meaning
  • Approves definitions and rules

Data Custodian

  • Operates data systems
  • Executes technical storage tasks
  • Meaning vs operations

Governance Operating Model Picker DA

  • One team decides everything → Centralized
  • Each unit decides alone → Decentralized
  • Local stewards, central council → Federated
  • Mix of central and local → Hybrid

Governance Roles DA

  • Data Steward Owns business meaning
  • Data Custodian Operates data systems
  • Data Governance Council Approves policy, standards
  • Data Mgmt Executive Leads governance program
  • Business Glossary Enterprise term definitions
  • Data Owner Accountable for asset

Governance vs Management DA

Governance

  • Authority and control
  • Sets policy direction

Management

  • Executes plans and practices
  • Runs daily operations
  • Authority vs execution

Governance Operating Models DA

  • Centralized Single team decides
  • Decentralized Business units decide
  • Federated Local stewards, central council
  • Governance Authority and control
  • Management Execution of plans

Model Levels DA

  • Conceptual Model Business terms only
  • Logical Model Attributes, keys, tech-neutral
  • Physical Model Platform-specific structures
  • Enterprise Data Model Subject areas enterprise-wide

Normalization and Notations DA

  • 1NF Atomic values only
  • 2NF No partial dependency
  • 3NF No transitive dependency
  • BCNF Tightens 3NF, multiple keys
  • IE Notation Crow's-foot relationships
  • Naming Standards Class word plus modifier

DQ Dimensions Mnemonic DA

  • CACTUV covers six DQ dimensions
  • C: Completeness present A: Accuracy correct C: Consistency agrees T: Timeliness fresh U: Uniqueness no dupes V: Validity conforms

Which DQ Dimension Failed DA

  • Value is missing → Completeness
  • Value is wrong → Accuracy
  • Systems disagree → Consistency
  • Data is stale → Timeliness
  • Records are duplicated → Uniqueness
  • Value breaks a rule → Validity

Data Quality Dimensions DA

  • Completeness Required values present
  • Accuracy Matches real-world truth
  • Consistency Systems agree
  • Timeliness Data fresh enough
  • Uniqueness No unintended duplicates
  • Validity Conforms to rules

DQ Improvement Process DA

  • Data Profiling Discovers actual characteristics
  • Root Cause Analysis Finds underlying source
  • DMAIC Define Measure Analyze Improve Control
  • PDCA Plan Do Check Act
  • Cost of Poor Quality Rework, churn, fines
  • DQ Scorecard Measurable rule conformance

Business vs Technical Metadata ME

Business Metadata

  • Ownership and definitions
  • Policies and business rules

Technical Metadata

  • Column data types
  • Table and object structure
  • Meaning vs structure

Metadata Types ME

  • Business Metadata Ownership, definitions, rules
  • Technical Metadata Structures, data types
  • Operational Metadata Load times, job status
  • Data Lineage Source-to-consumption traceability

Metadata Repository Architecture ME

  • Centralized Repository One physical store
  • Distributed Repository Metadata stays at source
  • Hybrid Repository Combines both patterns
  • CWM OMG DW/BI metadata standard

MDM Architecture Spectrum MA

  • Registry to Consolidation to Coexistence to Transactional
  • Registry: pointers only Consolidation: analytics copy only Coexistence: syncs both ways Transactional: hub is authoritative

Registry vs Transactional MDM MA

Registry

  • Pointers only
  • No source changes

Transactional

  • Hub is authoritative
  • Writes back to sources
  • Lightest touch vs heaviest

MDM Architecture Picker MA

  • No source changes allowed → Registry ( Pointers only )
  • Need analytics copy only → Consolidation ( No writeback )
  • Need sync both directions → Coexistence ( Persists and syncs )
  • Hub is system-of-record → Transactional ( Writes back to source )

MDM Architectures MA

  • Registry Pointers only, no copy
  • Consolidation Analytics copy, no writeback
  • Coexistence Persists and syncs back
  • Transactional Hub is system-of-record

Master Data vs Reference Data MA

Master Data

  • Core entities like Customer
  • Slowly changing over time

Reference Data

  • Small code lists
  • Constrains operational values
  • Core entity vs code list

MDM Core Concepts MA

  • Golden Record Trusted consolidated entity
  • Match-Merge Resolves duplicate identity
  • Survivorship Rules pick surviving values
  • Cross-Reference (xref) Maps IDs to master
  • Reference Data Small authoritative code lists

Warehouse Architecture Camps DA

  • Inmon normalizes first; Kimball dimensions first
  • Inmon: enterprise warehouse first Kimball: dimensional marts first ODS: near-real-time operational layer Mart: subject-area subset only

Inmon vs Kimball DA

Inmon CIF

  • Normalized warehouse first
  • Feeds marts downstream

Kimball Bus

  • Dimensional marts first
  • Conformed dimensions integrate
  • Top-down vs bottom-up

Warehouse Architecture Picker DA

  • Need normalized enterprise warehouse → Inmon CIF
  • Need fast dimensional delivery → Kimball Bus
  • Need near-real-time reporting → ODS
  • Need one subject-area subset → Data Mart

Warehouse Architectures DA

  • Inmon CIF Normalized enterprise warehouse first
  • Kimball Bus Dimensional marts, conformed dims
  • ODS Near-real-time operational layer
  • Data Mart Subject-area subset

Star vs Snowflake Schema DA

Star Schema

  • Denormalized dimensions
  • Fewer joins, faster

Snowflake Schema

  • Normalized dimensions
  • More joins, less redundancy
  • Speed vs storage efficiency

Dimensional Modeling DA

  • Star Schema Fact center, denormalized dims
  • Snowflake Schema Normalized dimension tables
  • Conformed Dimension Shared across fact tables
  • SCD Type 1 Overwrite, no history
  • SCD Type 2 New row, tracks history
  • Atomic Grain Lowest-level fact detail

BI Delivery DA

  • OLAP Drill-down, slice-and-dice
  • ELT Transform inside warehouse
  • ETL Transform before loading
  • Self-Service BI Certified datasets, curated marts

Architecture Frameworks DA

  • Zachman Framework Perspectives x aspects grid
  • TOGAF ADM Enterprise architecture method
  • Data Flow Diagram Movement between systems
  • Enterprise Data Architecture Subject areas, flows, stores

ECM and Records DO

  • ECM Enterprise content management
  • Retention Schedule Defines how long kept
  • e-Discovery Legal content search
  • GARP Principles ARMA records management principles
  • Taxonomy Classifies unstructured content

ETL vs ELT DA

ETL

  • Transform before load
  • Separate transform tier

ELT

  • Transform inside warehouse
  • Uses warehouse compute
  • Where transformation happens

Integration Patterns DA

  • ETL Extract, transform, load
  • CDC Captures source changes
  • ESB Message-based integration bus
  • Canonical Model Common integration message format
  • Virtualization Query without moving data

Security Fundamentals DA

  • CIA Triad Confidentiality, integrity, availability
  • RBAC Role-based access control
  • ABAC Attribute-based access control
  • Encryption Protects data confidentiality

Privacy Regulations DA

  • GDPR EU data privacy law
  • HIPAA US health data privacy
  • CCPA California consumer privacy act
  • Data Classification Labels sensitivity level

Storage and Operations DA

  • DBA Role Operates database platforms
  • NoSQL Non-relational database
  • Backup and Recovery Restores after failure
  • Performance Tuning Optimizes query speed

Big Data Concepts BI

  • Volume Data size scale
  • Velocity Speed of arrival
  • Variety Structured, unstructured mix
  • Veracity Trustworthiness of data
  • Data Lake Raw, schema-on-read storage

Data Ethics Concepts DA

  • Bias Systematic unfair skew
  • Fairness Equitable treatment of subjects
  • Transparency Explainable data use
  • Belmont Principles Respect, beneficence, justice

Environmental Factors Hexagon DA

  • Goals Activities Roles Deliverables Practices Tools
  • Goals: why it matters Activities: what gets done Roles: who does work Deliverables: outputs produced Practices: how it's done Tools: technology used

Environmental Factors and Maturity DA

  • Goals and Principles Why the work matters
  • Activities What work gets done
  • Deliverables Outputs of the work
  • Roles and Responsibilities Who performs the work
  • CMMI-DMM Data maturity model
  • EDM Council DCAM Capability assessment model

Common Traps

Steward ≠ Custodian

  • Steward owns business meaning ≠ Custodian operates the systems

Governance ≠ Management

  • Governance sets authority, policy ≠ Management executes plans, practices

Master Data ≠ Reference Data

  • Master: core changing entities ≠ Reference: small static code lists

Star Schema ≠ Snowflake Schema

  • Star denormalizes dimension tables ≠ Snowflake normalizes dimension tables

ETL ≠ ELT

  • ETL transforms before loading ≠ ELT transforms after loading

Data Quality ≠ Data Profiling

  • Quality measures rule conformance ≠ Profiling discovers actual characteristics

Policy ≠ Standard

  • Policy states business intent ≠ Standard sets measurable, testable rules

Golden Record ≠ Source Record

  • Golden record is trusted, consolidated ≠ Source record is raw, unmerged

Last Minute

  • 1. Four core areas: 11% each
  • 2. MRD and DW/BI: 10% each
  • 3. Five areas share 6% weight
  • 4. Three areas share 2% weight
  • 5. Steward owns meaning, not systems
  • 6. Custodian operates systems, not meaning
  • 7. MDM order: Registry Consolidation Coexistence Transactional
  • 8. Inmon normalizes; Kimball builds dimensionally
  • 9. Star denormalized; snowflake normalizes dimensions
  • 10. SCD Type 2 adds history rows
  • 11. Pass scores: 60/70/80 percent
  • 12. 100 questions, 90 minutes, open-book

DAMA Certified Data Management Professional (CDMP)

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