CDMP-RMD Reliable Test Sims & CDMP-RMD Exam Overview

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DAMA CDMP-RMD Exam Syllabus Topics:

TopicDetails
Topic 1
  • Reference Data Management (RDM) Lifecycle: The lifecycle of reference data management is covered in this topic.
Topic 2
  • Fundamentals of Reference and Master Data: The fundamental ideas of reference and master data are covered in this section, along with their definitions, distinctions, and significance in data management.
Topic 3
  • Data Stewardship: The management of reference and master data by data stewards is the focal point of this topic. It addresses concepts such as accuracy, consistency, and quality of data.
Topic 4
  • Master Data Management (MDM) Lifecycle: The phases of the MDM lifecycle such as planning, design, implementation, and continuous operation are covered in this section.
Topic 5
  • Data Quality Management: The methods and procedures for guaranteeing the quality of reference and master data are examined in this topic. Moreover, it focuses on validation, monitoring, and data cleansing.
Topic 6
  • MDM and RDM Tools and Technologies: The technologies and tools available for managing reference and master data are discussed in this section.

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DAMA Reference And Master Data Management Exam Sample Questions (Q78-Q83):

NEW QUESTION # 78
Choosing unreliable sources for data, which can cause data quality issues, is a result of:

  • A. Weak Master Data Management
  • B. No chance controls
  • C. Immature data architecture
  • D. Too much data
  • E. Too little data

Answer: A

Explanation:
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.


NEW QUESTION # 79
The ISO definition of Master Data quality is which of the following?

  • A. The degree to which the data's characteristics fulfill individual users' requirements
  • B. Identifies the company that created and owns the Master Data
  • C. Data is compliant to all international, country, and industry standards
  • D. Data meets all common requirements of all data users
  • E. Data meets the objective dimensions but not the subjective dimensions

Answer: A

Explanation:
The ISO definition of Master Data quality focuses on the degree to which the data's characteristics meet the requirements of individual users. This implies that quality is subjective and depends on whether the data is suitable and adequate for its intended purpose, fulfilling the specific needs of its users.
References:
* ISO 8000-8:2015 - Data quality - Part 8: Information and data quality: Concepts and measuring.
* DAMA-DMBOK: Data Management Body of Knowledge (2nd Edition), Chapter 13: Data Quality Management.


NEW QUESTION # 80
The ______development lifecycle is the best approach to follow for Reference & Master Data efforts.

  • A. Data-centric
  • B. System
  • C. Project
  • D. Agile
  • E. Software

Answer: A

Explanation:
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.


NEW QUESTION # 81
What is a registry as it applies to Master Data?

  • A. An index that points to Master Data in the various systems of record
  • B. A system to identify how data is used for transactions and analytics
  • C. Reconciled versions of an organization's systems
  • D. A starling point for matching and linking new records
  • E. Any data available during record creation

Answer: A

Explanation:
A registry in the context of Master Data Management (MDM) is a centralized index that maintains pointers to master data located in various systems of record. This type of architecture is commonly referred to as a
"registry" model and allows organizations to create a unified view of their master data without consolidating the actual data into a single repository. The registry acts as a directory, providing metadata and linkage information to the actual data sources.
References:
* DAMA-DMBOK2 Guide: Chapter 10 - Master and Reference Data Management
* "Master Data Management: Creating a Single Source of Truth" by David Loshin


NEW QUESTION # 82
Data Integration tor MDM and Reference data should:

  • A. Ignore minor changes because they will disrupt the entire system
  • B. Not allow ad-hoc changes to the data
  • C. Be designed to ensure timely extraction and distribution of data across the enterprise
  • D. Have one only one value for the same concept
  • E. Perform root analysis of data lineage at the time of integration

Answer: C

Explanation:
Data integration for Master Data Management (MDM) and reference data is a critical process that ensures data consistency, accuracy, and availability across the enterprise. The goal is to enable seamless data flow and access for various business functions.
* Timely Extraction and Distribution:
* Data integration processes must be designed to extract and distribute data efficiently and in a timely manner to ensure that all parts of the organization have access to up-to-date information.
* This involves implementing data pipelines and ETL (Extract, Transform, Load) processes that can handle large volumes of data and deliver it where needed without delays.
* Root Analysis of Data Lineage:
* While important for understanding data origins and transformations, root analysis of data lineage is typically part of data governance and auditing processes, not a primary focus during real-time integration.
* Ad-Hoc Changes:
* While controlled environments are important, integration processes should be flexible enough to accommodate necessary changes without compromising data integrity.
* Single Value for the Same Concept:
* Ensuring a single source of truth is essential but requires robust data governance and harmonization efforts rather than just focusing on integration.
* Ignoring Minor Changes:
* Ignoring changes can lead to data quality issues and discrepancies. Effective data integration should handle changes efficiently without causing disruptions.


NEW QUESTION # 83
......

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