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| Section | Weight | Objectives |
|---|---|---|
| Data Quality Assessment and Measurement | 25% | - Root Cause Analysis - Metrics and KPIs - Profiling Techniques - Benchmarking |
| Data Quality Fundamentals | 20% | - Definition of Data Quality - Dimensions of Data Quality (completeness, accuracy, consistency, timeliness, validity) - Data Quality in the Data Lifecycle |
| Data Quality Frameworks and Standards | 20% | - DAMA DMBOK Framework - Other Industry Standards - ISO 8000 (Data Quality) |
| Data Quality Improvement | 20% | - Validation Rules - Data Enrichment - Standardization - Data Cleansing Techniques |
| Data Quality Governance | 15% | - Policies and Procedures - Data Stewardship - Data Quality Roles and Responsibilities - Continuous Monitoring |
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NEW QUESTION # 117
A Data Quality team finds that 78% of customer complaints are caused by four defect categories out of twenty recorded categories. Which tool is most appropriate for prioritizing improvement activity?
Answer: D
Explanation:
Pareto analysis is the appropriate technique because it identifies the relatively small number of causes responsible for a large proportion of observed problems.
The team should arrange defect categories in descending order by frequency or business impact and calculate their cumulative contribution. If four categories account for 78% of complaints, prioritizing those categories is likely to generate substantially greater benefit than spreading equal effort across all twenty.
The value of Pareto analysis in Data Quality is not that every problem follows an exact 80/20 relationship.
Rather, it provides an evidence-based mechanism for focusing scarce remediation resources on the defect classes that generate the most significant outcomes.
Frequency should not be the only prioritization criterion. A rare defect could produce severe regulatory, safety, financial, or reputational consequences. Governance should therefore combine volume analysis with business impact and risk.
Once priority defects are selected, the team should perform root-cause analysis and introduce preventive controls instead of merely fixing individual records.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Statistical Quality Tools; Pareto Analysis; Issue Prioritization; Business Impact; Root-Cause Remediation.
NEW QUESTION # 118
Big data management requires:
Answer: E
Explanation:
DAMA-DMBOK2 establishes the guiding principle that Big Data management requires more discipline than relational data management. The reason is not simply data volume. Big Data environments combine large volumes with substantial variation in structure, source, velocity, semantics, and reliability. These characteristics increase the probability of uncontrolled duplication, inconsistent interpretation, poor provenance, inappropriate use, and undetected quality defects. DAMA's Big Data guidance specifically emphasizes disciplined management of metadata describing Big Data sources, their origins, and their value.
From a Data Quality perspective, scale does not make data fit for purpose. Large datasets still require defined quality expectations, profiling, monitoring, lineage, controlled transformations, ownership, and governance.
Metadata becomes particularly important because analysts must understand where datasets originated, how they were transformed, their meaning, and whether they are appropriate for a particular analytical objective.
Data Governance must consequently establish accountability, acceptable-use policies, security requirements, quality expectations, and escalation mechanisms. Statistical and automated controls are also essential because manual inspection becomes impractical at Big Data scale.
Neither larger budgets nor data-science certification substitutes for sound data-management discipline.
Likewise, reducing controls because data is large creates greater-not lower-operational and analytical risk.
Reference Topics: DAMA-DMBOK2 - Big Data and Data Science; Big Data Management Principles; Metadata Management; Data Governance; Chapter 13 - Fitness for Purpose and Data Quality Controls.
NEW QUESTION # 119
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 # 120
The need to manage data movement efficiently is a primary driver for:
Answer: B
Explanation:
The need to manage data movement efficiently is a primary business driver for Data Integration and Interoperability (DII). DAMA-DMBOK2 states this directly. Modern organizations operate hundreds or thousands of databases, applications, files, services, data stores, external interfaces, and analytical platforms.
Data must continually move among these environments, often across organizational boundaries.
Without disciplined integration management, data movement becomes fragmented, expensive, difficult to monitor, and highly dependent on duplicated point-to-point interfaces. DII addresses this problem by establishing controlled mechanisms for extraction, transformation, messaging, replication, orchestration, APIs, data virtualization, and other forms of information exchange.
From a Data Quality perspective, every movement of data presents an opportunity either to preserve quality or to damage it. Source-to-target mappings must maintain semantic meaning, transformations must be controlled, reference values must remain consistent, and lineage must show how values were altered.
Metadata Management therefore records mappings, interface definitions, transformation rules, and lineage.
Master Data Management uses integration mechanisms to distribute governed master and reference data consistently between systems.
Data Warehousing is a major consumer of integration capabilities, but the broader discipline whose explicit driver is efficient movement across systems is Data Integration and Interoperability.
Reference Topics: DAMA-DMBOK2 Chapter 8 - Business Drivers; Data Integration and Interoperability; Data Movement; Chapter 13 - Consistency, Integrity and Transformation Quality.
NEW QUESTION # 121
Which of the following is a directive that codifies principles and management intent into fundamental rules governing the creation, acquisition, integrity, security, quality, and use of data and information?
Answer: B
Explanation:
The definition describes Data Policies. DAMA-DMBOK2 states that data policies are directives that translate organizational principles and management intent into fundamental rules controlling how data is created, acquired, protected, maintained, assured for quality, and used.
The distinction between governance and policy is important. Data Governance is the broader authority and decision-making framework. Policies are one of the principal instruments produced or sponsored through that framework. They define what must or must not occur. Standards and procedures subsequently translate those policies into specific implementation requirements and operational methods.
For Data Quality, a policy might require business-critical data to have designated ownership, documented definitions, measurable quality rules, monitored thresholds, and formal remediation processes. The detailed thresholds themselves may reside in standards or rule repositories rather than in the high-level policy.
Metadata Management makes policy operational by documenting definitions, classifications, ownership, lineage and applicable quality rules. Master Data Management applies those policies to shared business entities and controlled reference values. Data Stewards then monitor compliance and escalate violations through governance mechanisms.
Data asset valuation is concerned with determining economic value, while Data Management encompasses the complete discipline. Neither provides the specific "directive" definition stated in the question.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Data Policies; Governance Principles; Standards and Procedures; Chapter 13 - Data Quality Governance; Metadata and Master Data interactions.
NEW QUESTION # 122
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