CDMP-RMD Practice Questions

Master Data relationships define how different master data entities are related to each other within

an organization. These relationships are crucial for understanding and managing the data effectively.

The types of master data relationships generally include hierarchies, groupings, and associations that

help in organizing and categorizing the data.

Customer Household:

This refers to grouping individual customers into a single household entity. It is commonly used in

consumer industries to understand the relationships and dynamics within a household.

Fixed-Level Hierarchy:

A hierarchy with a predetermined number of levels. Each level has a specific position and

relationship to other levels, such as organizational hierarchies or product categorization.

Ragged-Level Hierarchy:

Similar to fixed-level hierarchies, but with varying levels of depth. It accommodates entities that may

not fit neatly into a fixed-level structure, providing flexibility in the hierarchy.

Grouping based on common criteria:

This involves creating groups or segments of data based on shared attributes or criteria. For example,

grouping products by category or customers by region.

Survivorship (NOT a relationship):

Survivorship pertains to the process of determining the most accurate and relevant data when

multiple records exist for the same entity. It is a data quality and management process, not a type of

relationship.

Reference:

DAMA-DMBOK (Data Management Body of Knowledge) Framework

CDMP (Certified Data Management Professional) Exam Study Materials

Master Data Management (MDM) lifecycle management encompasses the processes and practices

involved in managing master data throughout its lifecycle, from creation to retirement. It ensures

that master data remains accurate, consistent, and usable.

Reconciling and Consolidating Data:

This process involves merging data from multiple sources to create a single, unified view of each

master data entity.

It ensures that duplicate records are identified and consolidated, maintaining data consistency.

Identifying Multiple Instances of the Same Entity:

This involves detecting and resolving duplicate records to ensure that each master data entity is

uniquely represented.

Tools and algorithms are used to identify potential duplicates based on matching criteria.

Identifying Improperly Matched or Merged Instances of Data:

This step involves reviewing and correcting any errors that occurred during the matching or merging

process.

Ensures that data integrity is maintained and that merged records accurately represent the

underlying entities.

Maintaining Cross-Reference to Enable Information Integration:

Cross-

link related data entities across different systems, enabling seamless information

integration.

This ensures that data can be consistently accessed and used across the organization.

Establishing Recovery and Backup Rules (NOT part of MDM Lifecycle Management):

While important for overall data management, recovery and backup rules pertain more to data

protection and disaster recovery rather than the specific processes of MDM lifecycle management.

Reference:

DAMA-DMBOK (Data Management Body of Knowledge) Framework

CDMP (Certified Data Management Professional) Exam Study Materials

Definitions and Context:

Data Profiling: This is the process of examining data from existing data sources and collecting

statistics or informative summaries about that data.

Master Data Management (MDM): Establishing MDM involves processes and technologies for

managing the non-transactional data entities of an organization.

Explanation:

Data profiling helps to understand the data's characteristics and quality by analyzing data values and

comparing them to defined valid values.

This process is crucial in establishing a Master Data Management (MDM) system as it ensures the

data adheres to the defined standards and is clean, accurate, and ready for integration into the MDM

system.

Reference:

DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition, Chapter 11: Master and

Reference Data Management.

Kimball,

R. & Caserta,

J. (2004). The Data Warehouse ETL Toolkit: Practical Techniques for Extracting,

Cleaning, Conforming, and Delivering Data.

Marketing is crucial to the success of a Master Data Management (MDM) program for several

reasons:

Encouraging Onboarding:

Master Data Onboarding: Effective marketing strategies can encourage different data sources to

integrate their master data into the MDM system, ensuring comprehensive data coverage.

Promoting Benefits to Leadership:

Leadership Buy-in: Marketing the benefits of MDM to organizational leadership can secure necessary

support and resources for the program. Highlighting efficiencies, cost savings, and improved

decision-making can be persuasive.

Growing Subscriber Base:

User Engagement: Promoting the MDM program can help grow the base of subscribers and users

who rely on the master data, ensuring the data is used effectively across the organization.

Ensuring Long-term Sustainability:

Sustainability: Continuous marketing helps maintain interest and investment in the MDM program,

ensuring its long-term sustainability and relevance within the organization.

Reference:

Data Management Body of Knowledge (DMBOK), Chapter 7: Master Data Management

DAMA International, "The DAMA Guide to the Data Management Body of Knowledge (DMBOK)"

Master data and metadata are both used to aggregate data, but master data requires that the

organization identifies or develops a trusted version of truth for each of its entities.

Trusted Version of Truth:

For effective master data management, an organization must establish a single, trusted version of

truth for each master data entity (e.g., customer, product).

This involves harmonizing data from various sources, resolving duplicates, and ensuring consistency

and accuracy.

Master Data:

Master data includes critical business information that provides context for business transactions and

analysis. It must be consistent, accurate, and up-to-date to support operational and analytical

processes.

Other Options:

Transaction Activity Data: Important for operational processes but not the focus for creating master

data.

One Set of Data as Source and Target: Not sufficient for managing master data.

Specific Application Solutions: While useful, they do not ensure the creation of a trusted version of

truth for master data.

Transaction Audit Data: Important for auditing but not central to master data creation.

Reference:

DAMA-DMBOK (Data Management Body of Knowledge) Framework

CDMP (Certified Data Management Professional) Exam Study Materials

The three primary categories of components in a Master Data Management (MDM) framework are

people, process, and technology. Here’s a detailed breakdown:

People:

Roles and Responsibilities: Involves defining roles such as data stewards, data owners, and data

governance committees who are responsible for managing and overseeing master data.

Skills and Training: Ensuring that the individuals involved have the necessary skills and training to

manage master data effectively.

Process:

Data Governance: Establishing policies, procedures, and standards for managing master data to

ensure its accuracy, consistency, and reliability.

Data Lifecycle Management: Processes for creating, maintaining, and retiring master data.

Technology:

MDM Tools and Platforms: Utilizing technology solutions to support the management of master data,

including data integration, data quality, and data management platforms.

Infrastructure: Ensuring the necessary technical infrastructure is in place to support MDM activities.

Reference:

Data Management Body of Knowledge (DMBOK), Chapter 7: Master Data Management

DAMA International, "The DAMA Guide to the Data Management Body of Knowledge (DMBOK)"

Ownership is a crucial principle in managing Reference and Master Data. Here’s an in-depth look at

why:

Organizational Ownership:

Unified Responsibility: Reference and Master Data are assets that span across various functions and

departments within an organization.

Consistency and Accuracy: Ensuring that data ownership is attributed to the organization prevents

silos and ensures data is consistently accurate and available across all departments.

Data Governance: Proper governance frameworks ensure that data is managed in a way that meets

the organization’s needs and complies with relevant regulations and standards.

Avoiding Departmental Silos:

Cross-functional Use: Different departments use and rely on Reference and Master Data, so

ownership by a single department can lead to conflicts and inconsistencies.

Holistic Management: Centralized ownership enables holistic data management practices, enhancing

data quality and usability across the organization.

Reference:

Data Management Body of Knowledge (DMBOK), Chapter 7: Master Data Management

DAMA International, "The DAMA Guide to the Data Management Body of Knowledge (DMBOK)"

The Master Data hub environment that serves as the system of record for Master Data is:

Consolidated Hub:

Central Repository: Acts as a central repository where master data is stored and managed.

Data Quality and Integration: Ensures data quality by integrating data from various source systems

and providing a single source of truth.

System of Record: Maintains the most accurate and up-to-date information about master data

entities.

Other Hub Types:

SOA (Service-Oriented Architecture): Focuses on providing a flexible architecture for integrating

services but not specifically a master data hub.

Two-Speed Hub: A hybrid approach, but not solely a system of record.

Source Hub: May refer to original source systems, not a consolidated system of record.

Registry: Primarily maintains

to data stored in other systems but not a comprehensive

system of record.

Reference:

Data Management Body of Knowledge (DMBOK), Chapter 7: Master Data Management

DAMA International, "The DAMA Guide to the Data Management Body of Knowledge (DMBOK)"

An Operational Level Agreement (OLA) is used for detailing collaboration principles, escalation, and

dispute resolution processes between MDM and its data suppliers. Here’s why:

Purpose of an OLA:

Collaboration Principles: Defines how MDM teams and data suppliers will collaborate, including

roles, responsibilities, and communication protocols.

Escalation Processes: Outlines the steps for escalating issues when standard resolution mechanisms

are insufficient, ensuring timely and effective problem resolution.

Dispute Resolution: Specifies methods for resolving disputes between parties, fostering a

cooperative and constructive working relationship.

Other Documents:

Business Requirements Document: Defines business needs and requirements but doesn’t typically

focus on operational collaboration.

Metadata Catalog: Describes metadata and data dictionaries, not collaboration principles.

Warranty: Provides guarantees on products or services, irrelevant to operational collaboration.

Operations Run Book: Details operational procedures and workflows, not specifically focused on

collaboration and dispute resolution.

Reference:

Data Management Body of Knowledge (DMBOK), Chapter 8: Data Quality Management

DAMA International, "The DAMA Guide to the Data Management Body of Knowledge (DMBOK)"

Deterministic matching, also known as exact matching, relies on predefined rules and algorithms to

parse and standardize data, ensuring that records are compared based on exact or standardized

values. This approach uses defined patterns and rules to determine whether two records represent

the same entity by matching key attributes exactly. Deterministic matching is precise and

unambiguous, making it a common approach for high-certainty matching tasks, although it can be

less flexible than probabilistic methods that allow for variations in data.

Reference:

DAMA-DMBOK2 Guide: Chapter 10 – Master and Reference Data Management

"Entity Resolution and Information Quality" by John R. Talburt