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

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)'

Answer : C

Authoritative listings of master data entities are essential for ensuring data accuracy and consistency across an organization.

Canonical Directories:

This term refers to standardized data models but is not typically used to describe authoritative listings of master data entities.

Entity Directories:

While this term could be used, it is not the most accurate or commonly used term for authoritative listings.

Reference Directories:

Reference directories are authoritative lists of master data entities such as companies, people, and products. They provide standardized, trusted, and verified data that organizations can rely on.

These directories ensure that everyone in the organization uses consistent and accurate data, supporting data quality and governance efforts.

Industry Directories:

These directories provide information specific to an industry but are not necessarily authoritative lists of master data entities.

Source Directories:

This term does not specifically refer to authoritative master data listings.

DAMA-DMBOK (Data Management Body of Knowledge) Framework

CDMP (Certified Data Management Professional) Exam Study Materials

Answer : C

Choosing unreliable sources for data can lead to significant data quality issues. This problem is often a symptom of underlying issues in data management practices.

Too Much Data:

While having excessive data can create challenges, it is not directly related to the reliability of data sources.

Immature Data Architecture:

An immature data architecture can contribute to various data issues, but it specifically relates to the overall design and infrastructure rather than the selection of data sources.

Weak Master Data Management (MDM):

MDM is crucial for ensuring data quality and consistency. Weak MDM practices can lead to poor data governance, lack of standardization, and the use of unreliable data sources.

Effective MDM involves establishing strong governance policies, data stewardship, and validation processes to ensure data is sourced from reliable and authoritative sources.

Too Little Data:

Insufficient data can be problematic but is not directly related to choosing unreliable data sources.

No Chance Controls:

This option is not a standard term in data management and does not directly address the issue of data source reliability.

DAMA-DMBOK (Data Management Body of Knowledge) Framework

CDMP (Certified Data Management Professional) Exam Study Materials

Answer : B

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.

DAMA-DMBOK (Data Management Body of Knowledge) Framework

CDMP (Certified Data Management Professional) Exam Study Materials

Answer : E

Metrics tied to Reference and Master Data Quality generally include:

Data Sharing Usage: Measures how often master data is accessed and used across the organization.

Rate of Change of Data Values: Tracks how frequently master data values are updated or modified.

Service Level Agreements (SLAs): Monitors adherence to agreed-upon service levels for data availability, accuracy, and timeliness.

Data Sharing Volume: Measures the volume of data shared between systems or departments.

Excluded Metric - Operational Functions: While operational functions are important, they are not typically considered metrics for data quality. Operational functions refer to the various tasks and processes performed by systems and personnel but do not directly measure data quality.

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

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

Answer : A

Key processing steps for successful MDM typically include:

Data Acquisition: The process of gathering and importing data from various sources.

Data Sharing & Stewardship: Involves ensuring data is shared appropriately across the organization and that data stewards manage data quality and integrity.

Entity Resolution: Identifying and linking data records that refer to the same entity across different data sources.

Data Model Management: Creating and maintaining data models that define how data is structured and related within the MDM system.

Excluded Step - Data Indexing: While indexing is a critical database performance optimization technique, it is not a primary processing step specific to MDM. MDM focuses on consolidating, managing, and ensuring the quality of master data rather than indexing, which is more about search optimization within databases.

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

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

Answer : C

Data Governance plays a pivotal role in the development of an enterprise-wide Master Data Management (MDM) strategy. Here's how:

Role of Data Governance:

Policy Development: Data Governance establishes policies and standards for data management to ensure data quality, security, and compliance.

Data Stewardship: Assigns roles and responsibilities to manage and oversee data assets across the organization.

MDM Strategy Support:

Conceptual Data Model:

Producing and managing an enterprise conceptual data model helps align the organization's data architecture with its business processes.

It provides a unified view of data entities, their relationships, and how data flows through various systems, ensuring consistency and accuracy.

Alignment with Business Goals: Ensures that MDM efforts support business objectives by providing a clear framework for data usage and governance.

Data Management Body of Knowledge (DMBOK), Chapter 3: Data Governance

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