Dama Reference And Master Data Management CDMP-RMD Exam Questions

Answer : A

Data profiling is a crucial activity in mapping source system data for MDM efforts. Data profiling involves analyzing data from source systems to understand its structure, content, and quality. Key steps include:

Data Assessment: Evaluating the data to identify patterns, inconsistencies, and anomalies.

Data Quality Analysis: Measuring the quality of data in terms of accuracy, completeness, consistency, and uniqueness.

Metadata Extraction: Extracting metadata to understand data definitions, formats, and relationships.

Data Cleansing: Identifying and correcting data quality issues to ensure that the data is suitable for integration into the MDM system.

By performing data profiling, organizations can gain insights into the current state of their data, identify potential issues, and develop strategies for data integration and quality improvement.

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

'Data Quality: The Accuracy Dimension' by Jack E. Olson.

Answer : D

The critical need of any Reference & Master Data effort is executive sponsorship. Executive sponsorship provides the necessary authority, visibility, and support for the MDM initiative. Key aspects include:

Strategic Alignment: Ensures that the MDM effort aligns with the organization's strategic goals and objectives.

Resource Allocation: Secures the required funding, personnel, and other resources needed for the MDM program.

Stakeholder Engagement: Facilitates engagement and commitment from key stakeholders across the organization.

Governance and Oversight: Provides governance and oversight to ensure the MDM program adheres to best practices and delivers value.

Without executive sponsorship, MDM initiatives often struggle to gain traction, secure necessary resources, and achieve long-term success.

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

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

Answer : D

In a Consolidated style MDM (Master Data Management) approach, data from multiple source systems is integrated into a single consolidated repository. This consolidated repository acts as the authoritative source for master data, often referred to as the 'system of record.' The system of record maintains the most accurate, up-to-date, and comprehensive view of master data. Key traits of this approach include:

Centralization: All master data is centralized in one repository, which simplifies data management and governance.

Consistency: Ensures that all users and systems access the same consistent set of master data.

Data Quality: Enhances data quality through data cleansing, deduplication, and validation processes.

Single Source of Truth: Serves as the definitive source for master data, reducing discrepancies and inconsistencies across the organization.

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

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

Answer : D

ISO 8000 is an international standard focused on data quality and information exchange. Its primary purpose is to define and measure the quality of data, ensuring that it meets the requirements for completeness, accuracy, and consistency. The standard provides guidelines for data quality management, including requirements for data governance, data quality metrics, and procedures for improving data quality over time. ISO 8000 is not meant to replace ISO 9000, which is focused on quality management systems, but to complement it by addressing data quality specifically.

ISO 8000: Overview and Benefits of ISO 8000, International Organization for Standardization (ISO)

DAMA-DMBOK2 Guide: Chapter 12 -- Data Quality Management

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.

Answer : B

Deterministic matching algorithms rely on exact matches between data fields to determine if records are the same. These algorithms require high-quality data because any discrepancy, such as typographical errors or variations in data entry, can prevent a match.

Characteristics of deterministic matching:

It has a discrete all or nothing outcome (C).

It matches exact character to character of one or more fields (D).

All identifiers being matched have equal weight (E).

Since deterministic matching is highly dependent on the quality of the data being matched, option B is incorrect.

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.

Answer : D

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.

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

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