Dama CDMP-RMD Exam Practice Test Instant Access

Question 1

What type of interactive system model is most often used for Master Data Management?

AHub-and-Spoke

BApplication-coupling

CPublish-Subscribe

DSynchronized interface

EPoint-to-point

Answer : A

The hub-and-spoke model is most often used for Master Data Management because it provides a central hub where master data is maintained, while the spokes represent different systems or applications that interact with the hub. This model allows for efficient management, synchronization, and distribution of master data across the enterprise, ensuring consistency and quality.

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

The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling by Ralph Kimball and Margy Ross.

Question 2

Why is a historical perspective of Master Data important?

AProvides an audit trail

BMay be required in litigation cases

CAttributes about Master Data subjects evolve over time

DEnables business analytics to determine the root cause of behavioral changes

EAll of the above

Answer : E

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.

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

CDMP Examination Study Materials.

Question 3

Does an organization have to agree to a single definition for Master Data?

ANo. master data can have many definitions

BYes. the key thing is to agree on a standard definition

CNo. technical data may have many definitions depending on the vendor

DNo. each department can have their own definitions for master data

ENo. financial data is master data but the definition is always changing

Answer : B

For effective Master Data Management, an organization must agree on a single, standard definition of master data. Here's why:

Consistency:

Single Definition: A standardized definition ensures consistency across different departments and systems.

Avoids Confusion: Prevents discrepancies and misunderstandings regarding what constitutes master data.

Data Quality and Governance:

Unified Approach: A single definition supports unified data governance policies and data quality standards.

Data Integration: Facilitates easier data integration and interoperability across various systems and processes.

Business Efficiency:

Aligned Objectives: Ensures all parts of the organization are aligned in their understanding and use of master data, leading to more efficient operations and decision-making.

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

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

Question 4

When 2 records are not matched when they should have been matched, this condition is referred to as:

AFalse Positive

BA True Positive

CA False Negative

DA True Negative

EAn anomaly

Answer : C

Definitions and Context:

False Positive: This occurs when a match is incorrectly identified, meaning records are deemed to match when they should not.

True Positive: This is a correct identification of a match, meaning records that should match are correctly identified as matching.

False Negative: This occurs when a match is not identified when it should have been, meaning records that should match are not matched.

True Negative: This is a correct identification of no match, meaning records that should not match are correctly identified as not matching.

Anomaly: This is a generic term that could refer to any deviation from the norm and does not specifically address the context of matching records.

The question asks about a scenario where two records should have matched but did not. This is the classic definition of a False Negative.

In data matching processes, this is a critical error because it means that the system failed to recognize a true match, which can lead to fragmented and inconsistent data.

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

ISO 8000-2:2012, Data Quality - Part 2: Vocabulary.

Question 5

What characteristics does Reference data have that distinguish it from Master Data?

AIt is more volatile and needs to be highly structured

BIt is always data from an outside source such as a governing body

CIt always has foreign database keys to link it to other data

DIt is less volatile, less complex, and typically smaller than Master Data sets

EIt provides data for transactions

Answer : C

Reference data and master data are distinct in several key characteristics. Here's a detailed explanation:

Reference Data Characteristics:

Stability: Reference data is generally less volatile and changes less frequently compared to master data.

Complexity: It is less complex, often consisting of simple lists or codes (e.g., country codes, currency codes).

Size: Reference data sets are typically smaller in size than master data sets.

Master Data Characteristics:

Volatility: Master data can be more volatile, with frequent updates (e.g., customer addresses, product details).

Complexity: More complex structures and relationships, involving multiple attributes and entities.

Size: Larger in size due to the detailed information and numerous entities it encompasses.

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

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

Question 6

For MDMs. what is meant by a classification scheme?

ACodes that represent a controlled set of values

BA vocabulary view covering a limited range of topics

CDescriptive language used to control objects

DA way of classifying unstructured data

Answer : A

In Master Data Management (MDM), a classification scheme refers to a structured way of organizing data by using codes that represent a controlled set of values. These codes help in categorizing and standardizing data, making it easier to manage, search, and analyze.

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

'Master Data Management and Data Governance' by Alex Berson and Larry Dubov.

Question 7

Within the Corporate Information Factory, what data is used to understand transactions?

AMaster Data and Unstructured Data

BInternal Data. Physical Schemas

CMaster Data. Reference Data, and External Data

DReference Data and Vendor Data

ESecurity Data and Master Data

Answer : C

In the context of the Corporate Information Factory, understanding transactions involves integrating various types of data to get a comprehensive view. Master Data (core business entities), Reference Data (standardized information), and External Data (information sourced from outside the organization) are essential for providing context and enriching transactional data.

DAMA-DMBOK: Data Management Body of Knowledge (2nd Edition), Chapter 3: Data Architecture and Chapter 11: Reference and Master Data Management.

'Building the Data Warehouse' by W.H. Inmon, which introduces the Corporate Information Factory concept.

Dama CDMP-RMD Reference And Master Data Management Exam Practice Test