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Free CDMP-RMD Exam Braindumps (page: 8)

Page 7 of 26
PDF with 100 Questions

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

  1. False Positive
  2. A True Positive
  3. A False Negative
  4. A True Negative
  5. An anomaly

Answer(s): C

Explanation:

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.


Reference:

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.



Bringing order to your Master Data would solve what?

  1. 20 40% of the need to buy new servers
  2. Distributing data across the enterprise
  3. The need for a metadata repository
  4. 60-80% of the most critical data quality problems
  5. Provide a place to store technical data elements

Answer(s): D

Explanation:

Definitions and Context:
Master Data Management (MDM): MDM involves the processes and technologies for ensuring the uniformity, accuracy, stewardship, semantic consistency, and accountability of an organization's official shared master data assets.
Data Quality Problems: These include issues such as duplicates, incomplete records, inaccurate data, and data inconsistencies.

Bringing order to your master data, through processes like MDM, aims to resolve data quality issues by standardizing, cleaning, and governing data across the organization. Effective MDM practices can address and mitigate a significant proportion of data quality problems, as much as 60-80%, because master data is foundational and pervasive across various systems and business processes.


Reference:

DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition, Chapter 11: Master and Reference Data Management.
Gartner Research, "The Impact of Master Data Management on Data Quality."



Reference Data Dictionaries are authoritative listings of:

  1. Master Data entities
  2. External sources of data
  3. Master Data sources
  4. Master Data systems of record
  5. Semantic rules

Answer(s): B

Explanation:

Definitions and Context:
Reference Data Dictionaries: These are authoritative resources that provide standardized definitions and classifications for data elements.
External Sources of Data: These are data sources that come from outside the organization and are used for various analytical and operational purposes.
Reference Data Dictionaries often contain listings and definitions for data that are used across different organizations and systems, ensuring consistency and interoperability. They typically include external data sources, which need to be standardized and understood in the context of the organization's own data environment.


Reference:

DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition, Chapter 11: Master and Reference Data Management.
ISO/IEC 11179-3:2013, Information technology - Metadata registries (MDR) - Part 3: Registry metamodel and basic attributes.



When establishing a MOM. what is the benefit of doing data profiling?

  1. Analyze data values and see how closely they correspond to a defined set of valid values
  2. Develop data migration approach
  3. Manage the design of the data warehouse
  4. Develop batch data flows for a scheduler
  5. Develop services to access, transform and deliver data

Answer(s): A

Explanation:

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.

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.


Reference:

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.






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