CDMP Exam Flashcards

Q: What is the passing score for the CDMP exam?

The same CDMP Fundamentals exam is graded on three thresholds: 60% earns Associate certification, 70% earns Practitioner level (when paired with two 70%-scored specialty exams), and 80% earns Master level (when paired with two 80%-scored specialty exams plus an experience assessment). There is no separate, harder exam for the higher levels - only a higher score on the same test.

Q: What is the CDMP pass rate?

DAMA International does not publish pass-rate statistics for the CDMP Fundamentals exam. Focus your prep on the 14 DAMA-DMBoK2 knowledge areas and their published weights rather than a target pass-rate percentage.

Q: Can I retake the CDMP exam immediately if I fail?

Yes. DAMA's official CDMP FAQ states you can retake an exam as many times as you wish, with each retake purchased separately for USD$200 plus the proctor fee, and no mandatory waiting period is imposed. If you score lower on a retake, your highest pass mark is always the one retained.

Q: How many questions are on the CDMP exam and how much time do I get?

CDMP Fundamentals is 100 multiple-choice questions in 90 minutes, delivered online-proctored via Honorlock. Candidates who need ESL accommodation can add 20 minutes for a 110-minute sitting.

Q: Do I need employer sponsorship to sit the CDMP exam?

No. Anyone can enroll in the CDMP Fundamentals exam directly through DAMA International or cdmp.info with no formal prerequisites at the Associate level. Practitioner and Master levels require additional specialty exams, and Master also requires a separate experience assessment.

Question 1

What does the DAMA Wheel show, and what sits at its center?

Accepted Answer

The DAMA Wheel arranges the DMBoK2 knowledge areas around a hub, with Data Governance at the center because it coordinates and sets policy for every other function. The surrounding areas - modeling, quality, security, and the rest - all depend on governance for standards and oversight.

Question 2

Data Steward vs. Data Custodian - who owns what?

Accepted Answer

The Data Steward is accountable for a data element's business meaning: its definition, valid values, and quality rules. The Data Custodian operates the technical systems that store the data. Meaning vs. operations is the key split.

Question 3

Data Governance Council

Accepted Answer

A cross-functional, decision-making body that approves data policies, standards, and stewardship assignments. It is not an operations team or an external auditor - it is where governance decisions get formally ratified.

Question 4

Name the three DMBoK governance operating models and their key difference.

Accepted Answer

Centralized (one team decides for the whole enterprise), Decentralized (each business unit decides independently, with no central body), and Federated (local stewards coordinate through a central council). Federated is the most common because it balances consistency with business-unit flexibility.

Question 5

How does DMBoK distinguish data Governance from data Management?

Accepted Answer

Governance is the exercise of authority and control over data assets - it sets policy and direction. Management is the execution of those plans day to day. In short: governance decides, management does.

Question 6

Business Glossary

Accepted Answer

An enterprise-wide catalog of approved business terms and their agreed definitions, owned by data stewards. It is the reference stewards use to resolve disputes about what a data element actually means.

Question 7

What distinguishes a conceptual, logical, and physical data model?

Accepted Answer

A conceptual model captures business entities and relationships in business terms only. A logical model adds attributes and keys and stays technology-neutral. A physical model specifies platform-specific tables, columns, and indexes ready to implement.

Question 8

1NF, 2NF, 3NF - what does each eliminate?

Accepted Answer

1NF requires atomic, non-repeating values in every column. 2NF removes partial dependencies on part of a composite key. 3NF removes transitive dependencies, where a non-key attribute depends on another non-key attribute instead of the key.

Question 9

How does BCNF differ from 3NF?

Accepted Answer

BCNF (Boyce-Codd Normal Form) tightens 3NF for tables with multiple overlapping candidate keys, requiring every determinant to be a candidate key. It catches anomalies that 3NF can miss when a table has more than one possible key.

Question 10

Enterprise Data Model

Accepted Answer

A single, integrated model showing an organization's major subject areas - Customer, Product, Order, and the like - and how they relate across the whole enterprise. Individual project models should align to it rather than reinvent structure.

Question 11

What is the standard DMBoK naming-standard pattern for a data element name?

Accepted Answer

A class word (Name, Code, Amount, Date) combined with one or more qualifying modifiers, producing consistent, self-describing names such as 'Customer Last Name' or 'Order Total Amount' across every model.

Question 12

Name the six core data quality dimensions in DMBoK2 (mnemonic: CACTUV).

Accepted Answer

Completeness (values present), Accuracy (matches reality), Consistency (systems agree), Timeliness (fresh enough), Uniqueness (no unintended duplicates), and Validity (conforms to defined rules).

Question 13

Completeness vs. Accuracy - what is the test for each?

Accepted Answer

Completeness asks whether a required value is present at all. Accuracy asks whether a present value correctly reflects the real-world fact. A field can be complete (filled in) yet still inaccurate.

Question 14

Data Profiling

Accepted Answer

The discovery process that examines actual data to reveal its real characteristics - value ranges, patterns, nulls, duplicates - performed before writing data quality rules, since assumptions about the data are often wrong.

Question 15

DMAIC

Accepted Answer

A Six Sigma-derived improvement cycle DMBoK applies to data quality: Define the problem, Measure the current state, Analyze the root cause, Improve the process, and Control to sustain the gains.

Question 16

Why does DMBoK emphasize root cause analysis over simply cleansing bad data?

Accepted Answer

Cleansing only fixes symptoms in existing records, so the same defect keeps recurring. Root cause analysis finds the upstream process or system causing the defect, so the fix prevents new bad data instead of only repairing old records.

Question 17

Cost of Poor Data Quality

Accepted Answer

The total business cost of bad data - rework, customer churn, regulatory fines, and poor decisions - used to build the business case for data quality investment, since quality work competes for budget like any other program.

Question 18

Business, Technical, and Operational metadata - what does each describe?

Accepted Answer

Business metadata covers definitions, ownership, and business rules. Technical metadata covers structures, data types, and formats. Operational metadata covers process facts like load times, job status, and record counts.

Question 19

Data Lineage

Accepted Answer

Metadata that traces a data element's path from its original source through every transformation to its final point of consumption. Essential for impact analysis and regulatory traceability.

Question 20

Centralized, Distributed, and Hybrid metadata repositories - what is the tradeoff?

Accepted Answer

Centralized stores all metadata in one physical repository - easy to search, harder to keep current. Distributed leaves metadata at its source system - always current, harder to search. Hybrid combines both to balance the tradeoff.

Question 21

CWM (Common Warehouse Metamodel)

Accepted Answer

The OMG-standard model for exchanging data warehouse and BI metadata between different tools and repositories, enabling metadata interoperability across vendors.

Question 22

Why does DMBoK treat Metadata Management as its own knowledge area rather than folding it into Data Governance?

Accepted Answer

Metadata is the 'data about data' infrastructure - definitions, lineage, and technical structure - that every other knowledge area (quality, modeling, governance) depends on to function. It needs its own architecture and repository strategy, not just a policy statement.

Question 23

Name the four MDM architecture styles, from lightest to heaviest touch.

Accepted Answer

Registry (pointers only, no copy of the data), Consolidation (an analytics copy, no write-back to sources), Coexistence (persists a copy and syncs both directions), and Transactional (the hub itself becomes the system-of-record and writes back to sources).

Question 24

Golden Record

Accepted Answer

The single, trusted, consolidated version of an entity - for example one Customer - produced by matching and merging records from multiple source systems. It is the output MDM is built to produce.

Question 25

Match-Merge vs. Survivorship - what does each step do?

Accepted Answer

Match-Merge identifies which source records represent the same real-world entity and combines them. Survivorship rules then decide which conflicting field value 'wins' and survives into the golden record.

Question 26

Master Data vs. Reference Data - what is the core distinction?

Accepted Answer

Master data covers core, slowly-changing business entities like Customer or Product. Reference data is small, static code lists - country codes, status codes - that constrain and validate the values used across systems.

Question 27

Cross-Reference (xref) table

Accepted Answer

A mapping table that links each source system's local identifier for an entity to the master or golden record's identifier, letting the hub trace a golden record back to every contributing source.

Question 28

Inmon CIF vs. Kimball Bus - what is the architectural difference?

Accepted Answer

Inmon's Corporate Information Factory builds a normalized enterprise warehouse first, then feeds subject-specific marts downstream (top-down). Kimball's Bus architecture builds dimensional marts first, integrated by conformed dimensions (bottom-up).

Question 29

Star Schema vs. Snowflake Schema - what is the tradeoff?

Accepted Answer

A star schema denormalizes dimension tables around a central fact table, giving fewer joins and faster queries. A snowflake schema normalizes the dimensions into sub-tables, saving storage but requiring more joins.

Question 30

Slowly Changing Dimension Type 1 vs. Type 2 - how do they handle a changed attribute?

Accepted Answer

Type 1 overwrites the old value, keeping no history. Type 2 adds a new row with new effective dates, preserving the full history of the attribute's changes over time.

Question 31

Conformed Dimension

Accepted Answer

A dimension - such as Date or Customer - built once with consistent keys and attributes, then shared across multiple fact tables and data marts so results can be compared and combined enterprise-wide.

Question 32

In the BI/warehouse context, when does an organization favor ELT over ETL?

Accepted Answer

ELT loads raw data first and transforms it using the warehouse engine's own compute power. It is favored when the target platform (for example a cloud warehouse) has cheap, scalable processing, letting transformation logic run close to the stored data.

Question 33

Zachman Framework

Accepted Answer

An enterprise architecture classification grid crossing six perspectives (Planner, Owner, Designer, Builder, Subcontractor, User) with six interrogatives (What, How, Where, Who, When, Why). It organizes architecture artifacts - it does not prescribe a delivery method.

Question 34

Enterprise Data Architecture

Accepted Answer

A blueprint identifying an organization's subject areas, how data flows between systems, and where it is stored. It is the structural map that individual data models and integration designs must align to.

Question 35

How does DMBoK's Data Architecture relate to TOGAF?

Accepted Answer

Data Architecture is treated as one domain within the broader enterprise architecture practice. TOGAF's ADM provides the overall enterprise-architecture method, while DMBoK's Data Architecture knowledge area focuses specifically on data-related structures within that method.

Question 36

ECM (Enterprise Content Management)

Accepted Answer

The strategies, methods, and tools used to capture, manage, store, preserve, and deliver unstructured content - documents, images, emails - across its lifecycle. Distinct from structured database records management.

Question 37

GARP Principles

Accepted Answer

ARMA International's Generally Accepted Recordkeeping Principles - accountability, transparency, integrity, protection, compliance, availability, retention, and disposition - the standard used to benchmark a records management program.

Question 38

Retention Schedule vs. e-Discovery - how do they relate?

Accepted Answer

A retention schedule defines how long each record type must legally or operationally be kept before disposal. e-Discovery is the process of searching and producing content - often beyond the schedule - in response to litigation or investigation.

Question 39

ETL vs. ELT - where does transformation happen in each?

Accepted Answer

ETL transforms data in a separate processing tier before loading it into the target. ELT loads raw data into the target first and transforms it there, using the target platform's own compute engine.

Question 40

CDC (Change Data Capture)

Accepted Answer

A technique that captures only the rows that changed in a source system - inserts, updates, deletes - since the last extract, rather than re-extracting the entire source. It reduces load and enables near-real-time integration.

Question 41

Canonical Model

Accepted Answer

A common, neutral message or data format used as the 'middleman' in integration, so each system only needs to map to and from one shared format instead of maintaining a point-to-point mapping with every other system.

Question 42

CIA Triad

Accepted Answer

The three foundational data-security goals: Confidentiality (only authorized access), Integrity (data isn't improperly altered), and Availability (data is accessible to those who need it when they need it).

Question 43

RBAC vs. ABAC - how do they grant access differently?

Accepted Answer

RBAC (Role-Based Access Control) grants permissions based on a user's assigned role. ABAC (Attribute-Based Access Control) grants access based on a combination of attributes - user, resource, environment - enabling finer-grained, contextual rules.

Question 44

GDPR, HIPAA, and CCPA - what does each regulate?

Accepted Answer

GDPR is the EU's general data-privacy law covering personal data broadly. HIPAA protects health information in the United States. CCPA gives California consumers specific rights over the personal data businesses collect about them.

Question 45

DBA (Database Administrator) role

Accepted Answer

The technical role responsible for operating database platforms - installation, configuration, performance tuning, backup and recovery, and availability. Distinct from the Data Steward's business-meaning accountability.

Question 46

NoSQL databases

Accepted Answer

Non-relational database types - document, key-value, column-family, graph - built for flexible schemas and horizontal scaling. Chosen over relational databases when data is unstructured, high-volume, or rapidly changing in shape.

Question 47

Backup and Recovery

Accepted Answer

The operational practice of copying data so it can be restored after loss or corruption. DMBoK ties recovery objectives to two metrics: Recovery Point Objective (how much data loss is acceptable) and Recovery Time Objective (how fast systems must be restored).

Question 48

The Four Vs of Big Data

Accepted Answer

Volume (data size at scale), Velocity (speed of arrival), Variety (structured and unstructured mix), and Veracity (trustworthiness of the data) - the characteristics DMBoK uses to define when data qualifies as 'big data.'

Question 49

Belmont Principles applied to data ethics

Accepted Answer

Respect for Persons, Beneficence, and Justice - three ethical principles DMBoK borrows from human-subjects research to evaluate whether a data use is ethical, alongside DMBoK's own themes of bias, fairness, and transparency.

Question 50

DMBoK's Environmental Factors Hexagon

Accepted Answer

The six factors DMBoK says apply to every knowledge area: Goals and Principles, Activities, Deliverables, Roles and Responsibilities, Practices and Methods, and Tools. Used to fully describe how any data management function operates.

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DAMA Certified Data Management Professional (CDMP)