CDMP-RMD Exam Dumps - DAMA CDMP Questions and Answers

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 references to data stored in other systems but not a comprehensive system of record.

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)"

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.

Explanation:

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

References:

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.

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

Information Governance involves managing and controlling the data assets of an organization, addressing the 'what', 'how', and 'why'.

'What' pertains to Data Governance, which defines policies and procedures for data management.

'How' relates to Information Security, ensuring that data is protected and secure.

'Why' is about Compliance, ensuring that data management practices meet legal and regulatory requirements.

References:

DAMA-DMBOK: Data Management Body of Knowledge (2nd Edition), Chapter 1: Data Governance.

"Information Governance: Concepts, Strategies, and Best Practices" by Robert F. Smallwood.

Managing Master Data involves several key activities, primarily focusing on:

Structured and Unstructured Data:

Structured Data: Managing well-defined data types, such as relational databases, where data is organized into tables and fields.

Unstructured Data: Handling data that does not have a predefined format or structure, such as emails, documents, and multimedia files.

Comprehensive Management:

Data Integration: Ensuring that data from various sources, both structured and unstructured, is integrated into the master data repository.

Data Quality: Implementing processes and tools to maintain high data quality for both structured and unstructured data.

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)"

The Landing Zone is typically used for ingesting raw source system data.

Data Architecture Component:The landing zone is where raw data from various source systems is initially collected and stored.

Function:It acts as a staging area before the data undergoes further processing, transformation, or analysis.

References:

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

CDMP Study Guide

Reference data and master data serve different purposes within an organization, and storing them in separate repositories can be beneficial for managing them effectively.

Reference Data:

Reference data is used to classify or categorize other data. Examples include code tables, taxonomies, and standard lists like country codes or industry classifications.

It is often less volatile and has a higher degree of standardization.

Master Data:

Master data refers to the core business entities that are essential for operations, such as customers, products, employees, and suppliers.

It is often more dynamic and requires frequent updates to ensure accuracy and consistency across systems.

Separate Repositories:

Storing reference and master data in separate repositories allows for tailored management strategies, governance, and security measures suited to their specific needs.

This approach can improve performance, data quality, and accessibility by reducing complexity and focusing resources on maintaining each type of data appropriately.

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.

A catalog where products are organized by category is an example of a taxonomy. Here’s why:

Definition of Taxonomy:

Classification System: Taxonomy refers to the practice and science of classification. It involves organizing items into hierarchical categories based on their relationships and similarities.

Example: In the context of a product catalog, taxonomy is used to classify products into categories and subcategories, making it easier to browse and find specific items.

Application in Product Catalogs:

Categorization: Products are grouped into logical categories (e.g., Electronics, Clothing, Home Appliances) and subcategories (e.g., Smartphones, Laptops, Televisions).

Navigation and Search: Helps users navigate the catalog efficiently and find products quickly by narrowing down categories.

References:

Data Management Body of Knowledge (DMBOK), Chapter 9: Data Architecture

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

Entity resolution is a critical step in the MDM process that identifies whether different data records refer to the same real-world entity. This ensures that each entity is uniquely represented within the master data repository.

Data Model Management:

Focuses on defining and maintaining data models that describe the structure, relationships, and constraints of the data.

Data Acquisition:

Involves gathering and bringing data into the MDM system but does not deal with resolving entities.

Entity Resolution:

This process involves matching and linking records from different sources that refer to the same entity. Techniques such as deterministic matching (based on exact matches) and probabilistic matching (based on similarity scores) are used.

Entity resolution helps in deduplication and ensuring a single, unified view of each entity within the MDM system.

Data Sharing & Stewardship:

Focuses on managing data access and ensuring that data is shared responsibly and accurately.

Data Validation, Standardization, and Enrichment:

Ensures data quality by validating, standardizing, and enriching data but does not directly address entity resolution.