Free DAMA CDMP-RMD Practice Exam with Questions & Answers

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.

References:

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.

The data-centric development lifecycle is best suited for Reference & Master Data efforts because it prioritizes data integrity, quality, and governance throughout the entire development process. This approach ensures that reference and master data are consistently managed, maintained, and leveraged across various systems and applications.

References:

DMBOK (Data Management Body of Knowledge), 2nd Edition, Chapter 11: Reference & Master Data Management.

DAMA International Guide to the Data Management Body of Knowledge (DAMA-DMBOK Guide), 2nd Edition.

MDM matching algorithms benefit from various data characteristics but do not benefit from "Structural heterogeneity of data elements."

Matching Algorithms:These are used in MDM to identify and link data records that refer to the same entity across different systems.

Data Characteristics:

Distinctiveness:Helps in accurately matching records.

Common Data Points:Aids in the comparison process.

Comparability:Facilitates effective matching.

Validity:Ensures the data is accurate and reliable.

Structural Heterogeneity:Different structures can complicate the matching process, making it harder to align data.

References:

DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition.

CDMP Study Guide

The registry style is the most difficult MDM style to implement data governance due to its reliance on maintaining a central registry of master data without consolidating data physically. This method makes it challenging to ensure consistent governance across disparate systems since data remains distributed and only loosely connected via the registry.

References:

DMBOK (Data Management Body of Knowledge), 2nd Edition, Chapter 11: Reference & Master Data Management.

Master Data Management: Creating a Single Source of Truth by David Loshin.

Data profiling is a crucial activity in mapping source system data for MDM efforts. Data profiling involves analyzing data from source systems to understand its structure, content, and quality. Key steps include:

Data Assessment: Evaluating the data to identify patterns, inconsistencies, and anomalies.

Data Quality Analysis: Measuring the quality of data in terms of accuracy, completeness, consistency, and uniqueness.

Metadata Extraction: Extracting metadata to understand data definitions, formats, and relationships.

Data Cleansing: Identifying and correcting data quality issues to ensure that the data is suitable for integration into the MDM system.

By performing data profiling, organizations can gain insights into the current state of their data, identify potential issues, and develop strategies for data integration and quality improvement.

References:

DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition.

"Data Quality: The Accuracy Dimension" by Jack E. Olson.

Sharing reference and master data across an enterprise requires effective collaboration and communication among various stakeholders within the organization.

Staging Area:

A staging area can be used for intermediate data storage during processing but is not a requirement for sharing data.

Maintaining and Storing History Records:

Historical records are important for auditing and tracking changes but do not directly facilitate the sharing of current reference and master data.

Collaboration Between Multiple Parties Internal to the Organization:

Effective sharing of master and reference data requires collaboration among different departments and stakeholders to ensure data consistency, quality, and governance.

This includes establishing clear communication channels, defining roles and responsibilities, and ensuring alignment on data standards and practices.

Identification of Business Key and Surrogate Keys:

Keys are important for data integration and linking but do not by themselves ensure effective sharing of data.

Creation of Foreign Keys to Support Dimensions:

Foreign keys are used in relational databases to link tables but are not specifically required for the sharing of master data.

Historical Perspective of Master Data:Maintaining historical data about master data objects is crucial for various reasons.

Reasons for Importance:

Provides an audit trail:Keeping historical data allows organizations to track changes and understand the evolution of data over time, which is essential for auditing purposes.

May be required in litigation cases:Historical data can serve as evidence in legal disputes, demonstrating the state of data at specific points in time.

Attributes about Master Data subjects evolve over time:As entities change, such as customers moving or changing names, maintaining historical data allows for accurate tracking of these changes.

Enables business analytics to determine the root cause of behavioral changes:Historical data can help in analyzing trends and identifying reasons for changes in business metrics or customer behavior.

Conclusion:All the provided reasons collectively highlight the importance of maintaining a historical perspective of master data.

References:

DMBOK Guide, sections on Master Data Management and Data Governance.

CDMP Examination Study Materials.

Entities such as Customer, Product, and Employee are typical examples of master data that need to be managed.

Master Data Entities:These are the key data objects around which business transactions are conducted.

Examples:

Customer:Central to sales and service operations.

Product:Essential for inventory and sales management.

Employee:Critical for HR and payroll systems.

References:

DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition.

CDMP Study Guide

A global identifier is used to link multiple references to the same entity across different systems or datasets. Here’s why:

Purpose of Global Identifier:

Unique Identification: Provides a unique identifier that can be used to recognize the same entity across disparate systems and datasets.

Consistency: Ensures that different references or records pointing to the same entity are consistently identified and managed.

Linking Equivalent References:

Equivalent References: Global identifiers link references that are equivalent, meaning they represent the same real-world entity even if the data is stored differently in various systems.

Entity Resolution: Helps in resolving different records to a single entity, ensuring data consistency and accuracy.

Example:

Customer Records: A customer might be listed in different systems (CRM, billing, support) with slightly different details. A global identifier links these records to recognize them as the same customer.

References:

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

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

Management of Reference and Master Data aims to mitigate the challenges of disparate data, which typically arise from:

Organic Growth:

Unplanned Expansion: Over time, organizations often develop new systems and applications organically, leading to isolated and redundant data stores.

Inconsistent Data: These disparate systems often result in inconsistent and unreliable data.

Isolated Systems:

Siloed Applications: Independent systems that do not communicate effectively with each other can lead to multiple versions of the same data.

Lack of Integration: Without proper integration, data consistency and quality suffer.

Mergers and Acquisitions:

Combining Systems: Mergers and acquisitions introduce the challenge of integrating different data systems and standards.

Data Redundancy: Newly acquired systems often come with their own data sets, leading to redundancy and conflicts.

References:

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

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