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| Section | Weight | Objectives |
|---|---|---|
| Data Quality Improvement | 20% | - Validation Rules - Data Enrichment - Standardization - Data Cleansing Techniques |
| Data Quality Governance | 15% | - Continuous Monitoring - Data Quality Roles and Responsibilities - Policies and Procedures - Data Stewardship |
| Data Quality Assessment and Measurement | 25% | - Metrics and KPIs - Profiling Techniques - Benchmarking - Root Cause Analysis |
| Data Quality Frameworks and Standards | 20% | - Other Industry Standards - ISO 8000 (Data Quality) - DAMA DMBOK Framework |
| Data Quality Fundamentals | 20% | - Data Quality in the Data Lifecycle - Definition of Data Quality - Dimensions of Data Quality (completeness, accuracy, consistency, timeliness, validity) |
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NEW QUESTION # 90
What is the purpose of Data Governance?
Answer: C
Explanation:
The precise DAMA-DMBOK2 purpose of Data Governance is to ensure that data is managed properly according to policies and best practices. DMBOK2 distinguishes this governance purpose from the broader objective of Data Management, which is concerned with obtaining value from data throughout its lifecycle.
Governance establishes the authority and control framework within which operational data-management functions work. This includes strategy, policy, standards, accountability, stewardship, compliance, issue escalation, quality oversight, and decision rights. DMBOK2 specifically identifies governance responsibilities involving policies for metadata, access, security and quality; setting Data Quality and Data Architecture standards; and providing oversight and corrective action.
This distinction is critical for Data Quality. Data Quality Management performs activities such as profiling, measurement, root-cause analysis, cleansing and monitoring. Data Governance determines who has authority, which requirements are mandatory, what thresholds are acceptable, which issues require escalation, and who owns remediation decisions.
Metadata Management supports governance by recording authoritative definitions, lineage, ownership and quality rules. Master Data Management operationalizes governance for shared business entities and reference domains.
Options B, C and D describe legitimate aspects of wider Data Management, but they do not state the specific purpose assigned to Data Governance by DMBOK2.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Introduction; Goals and Principles; Governance Oversight; Chapter 13 - Data Quality and Governance.
NEW QUESTION # 91
The role of Metadata in Data Management is:
Answer: E
Explanation:
The broad role of Metadata is to help organizations understand their data, systems, and workflows. Metadata provides the context required to interpret data correctly and connect business meaning with technical implementation and operational processing. This answer is also explicitly identified in DAMA-oriented certification material.
Business metadata explains concepts, definitions, rules, ownership, stewardship, and terminology. Technical metadata describes tables, columns, datatypes, interfaces, transformations, and system structures. Operational metadata captures information about processing, execution, schedules, and usage. Together, these categories explain what information means, where it resides, how it moves, and how it is used.
Metadata can certainly improve decision-making, support Big Data solutions, group concepts, and assist reporting, but those are secondary outcomes. Option C expresses its enterprise-wide role most comprehensively.
Its connection to Data Quality is fundamental. Quality cannot be assessed consistently without metadata explaining the intended meaning and acceptable characteristics of a data element. Lineage allows a defect to be traced to its origin, while business definitions establish the context for accuracy and validity measurements.
Reference Topics: DAMA-DMBOK2 Metadata Management - Business, Technical and Operational Metadata; Data Lineage; Business Glossary; Chapter 13 - Data Quality Rules and Metadata Dependencies.
NEW QUESTION # 92
Business continuity is an aspect of Governance. What should a business continuity plan include?
Answer: C
Explanation:
A Business Continuity Plan is fundamentally concerned with maintaining essential business operations when normal services are disrupted. Therefore, the correct response is that it outlines how the business will continue operating during an unplanned disruption.
Within the DAMA-DMBOK2 framework, continuity concerns interact particularly with Data Governance, Data Storage and Operations, Data Security, and operational metadata. The DMBOK2 index explicitly identifies Business Continuity Plans and associates them with operational resilience and recovery concepts. A continuity plan must address how critical processes, information assets, personnel, technology, dependencies, and recovery arrangements will sustain or restore required business capability. The established definition of a BCP likewise centers on continued operation during unplanned service disruption.
Option E is incomplete: identifying potential disruptions belongs to risk assessment and continuity planning, but merely listing disruptions does not explain how operations will continue. Options B and D concern communications rather than continuity itself.
From a Data Quality perspective, continuity controls also protect availability, timeliness, integrity, and reliability. Recovery arrangements must ensure that restored data is complete, current enough for business use, and reconciled against authoritative sources after disruption.
Reference Topics: DAMA-DMBOK2 - Data Governance; Data Storage and Operations; Business Continuity; Disaster Recovery; Backup and Recovery; Chapter 13 - Timeliness, Integrity and Fitness for Purpose.
NEW QUESTION # 93
A business case for adding a new master data management solution is dependent on achieving greater value from:
Answer: C
Explanation:
An MDM investment is justified when the business value obtained from centralized coordination of shared data exceeds the additional cost of managing and integrating another controlled representation of that data.
This is the economic trade-off expressed by option C and is also the intended answer in published versions of this DAMA question.
DAMA-DMBOK2 positions Master Data Management as a mechanism for improving consistency and control over enterprise entities such as customers, products, suppliers, employees, and locations. Uncoordinated local copies create redundant maintenance, inconsistent definitions, duplicate records, reconciliation costs, and integration complexity. DAMA sources emphasize that controlling shared master and reference data reduces both cost and risk generated by inconsistencies between systems.
However, introducing an MDM repository also incurs costs: integration, stewardship, matching, survivorship logic, synchronization, governance, maintenance, and potentially another physical copy of shared information.
The business case must therefore demonstrate that improved coordination, quality, reuse, reporting, and operational consistency outweigh those costs.
MDM does not require all contributing systems to be decommissioned, nor does it eliminate Reference Data Management. Similarly, "mining golden records" is not the fundamental economic basis for MDM.
Reference Topics: DAMA-DMBOK2 Chapter 10 - Master Data Management; Business Drivers; Shared Data; Central Coordination; Data Integration; Data Quality and Stewardship.
NEW QUESTION # 94
A Data Quality team wants to understand whether defect rates are improving month by month rather than viewing only the latest percentage. Which approach is most useful?
Answer: B
Explanation:
Trend analysis is most useful because it shows how a quality metric changes over time rather than presenting a single isolated measurement.
A current defect rate of 4% has different implications depending on whether the previous months were 12%,
8%, and 6%, or 1%, 2%, and 3%. The first pattern indicates improvement; the second indicates deterioration.
Trend analysis therefore provides context for management decisions, helps determine whether remediation is producing sustained results, and can identify emerging degradation before thresholds are breached.
For stable, regularly measured processes, statistical control charts can add further value by distinguishing routine variation from signals that warrant investigation.
Metrics should be measured consistently over time. Changing definitions, populations, or calculation methods without documenting them can make apparent trends misleading.
Metadata should preserve metric definitions, threshold changes, and measurement logic to ensure comparability.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Monitoring; Trend Analysis; Statistical Control; Metrics; Continuous Improvement.
NEW QUESTION # 95
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