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| Section | Weight | Objectives |
|---|---|---|
| Tools, Techniques and Implementation | 20% | - Business value and ROI - Data quality in lifecycle management - Data quality tools and technologies |
| Data Quality Improvement | 20% | - Root cause analysis - Data cleansing and standardization - Monitoring and continuous improvement |
| Data Quality Assessment and Profiling | 25% | - Data profiling techniques - Assessment methods and tools - Defining quality rules and metrics |
| Data Quality Concepts and Principles | 15% | - Data quality dimensions - Definition and importance of data quality - Data quality in DMBOK framework |
| Data Quality Strategy and Governance | 20% | - Data quality strategy and policy - Roles and responsibilities - Integration with data governance |
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NEW QUESTION # 103
A Data Quality process has remained within statistical control limits for six months, but management wants the average defect rate reduced further. What is the most appropriate conclusion?
Answer: D
Explanation:
A process can be statistically stable yet still perform at an unacceptable quality level. Statistical control means that variation is predictable within the established process; it does not mean that the average level of defects satisfies business expectations.
For example, a process may consistently produce a 3% defect rate with very little month-to-month variation.
If the business requirement is below 0.5%, the process is stable but incapable of meeting the desired quality level without improvement.
The appropriate response is therefore deliberate process improvement aimed at changing the underlying process and reducing its baseline defect rate. This differs from reacting to random individual observations within normal control limits.
After improvement, new performance data should be collected and control parameters reevaluated once the revised process reaches a stable state.
DAMA's revised Chapter 13 explicitly maps Shewhart and Deming improvement-cycle stages to Data Quality processes and preserves Statistical Process Control as supporting material.
The central principle is that control and capability are different questions: control asks whether the process is stable; business quality requirements determine whether that stable performance is good enough.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Statistical Process Control; Process Stability; Continuous Improvement; Shewhart/Deming Cycle; Thresholds.
NEW QUESTION # 104
Data governance represents:
Answer: E
Explanation:
DAMA-DMBOK2 explicitly states that Data Governance represents an inherent separation of duty between oversight and execution. It illustrates the principle through an analogy with financial governance: an auditor exercises oversight over financial processes without personally executing financial management. Similarly, Data Governance ensures that data is properly managed but does not itself perform every operational Data Management activity.
This separation is fundamental because governance must retain sufficient independence to establish rules, monitor compliance, resolve conflicts, assign accountability, and evaluate whether operational teams are managing data according to approved policies and standards.
Option B describes activities in which governance and Data Quality practitioners may collaborate, but it is not the defining governance principle. Option D is also incorrect because federated governance is only one possible operating model; DMBOK2 also recognizes centralized and replicated approaches. Option E describes an organizational implementation, not the conceptual distinction.
Within Data Quality Management, this means that governance may approve quality policies, Critical Data Elements, acceptable thresholds, stewardship structures, and escalation procedures, while DQ analysts and operational teams perform profiling, cleansing, monitoring, and remediation.
Maintaining this division reduces conflicts of interest and establishes accountability for whether data- management activities achieve agreed business objectives.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Essential Concepts; Oversight versus Execution; Governance Operating Models; Chapter 13 - Data Quality Governance and Operational Responsibilities.
NEW QUESTION # 105
A dashboard reports that Customer_Email Completeness is 98.5%, while the approved threshold is 99.5%.
What should the 98.5% figure be interpreted as?
Answer: B
Explanation:
The 98.5% value is a measured Data Quality result, and because it is below the approved 99.5% threshold, it represents a threshold breach.
A Data Quality metric quantifies observed performance. A threshold establishes the level of performance the business has agreed is acceptable. These should not be confused. The metric answers how are we performing?; the threshold answers is that performance acceptable?
The 98.5% completeness result says nothing about whether the populated email addresses are accurate. A field can achieve high completeness while containing incorrect or invalid values. Other rules may therefore be required for email syntax, uniqueness, deliverability, or factual correctness.
A threshold breach should trigger a predefined response appropriate to the criticality of the data. This may involve investigation, escalation, remediation, trend review, or root-cause analysis.
Metrics, calculation logic, measurement population, thresholds, ownership, and escalation rules should be documented as metadata so dashboard results remain interpretable and reproducible.
DAMA's framework explicitly places metrics, error detection, cleansing, and quality management within Data Quality Management.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Quality Metrics; Thresholds; Scorecards; Completeness; Monitoring.
NEW QUESTION # 106
Three common interaction models for data integration are:
Answer: A
Explanation:
DAMA-DMBOK2 identifies Point-to-Point, Hub-and-Spoke, and Publish-Subscribe as three common interaction models used to transfer data between systems.
In Point-to-Point, one system communicates directly with another. It is straightforward for a small number of systems but becomes difficult to maintain as interfaces proliferate. In Hub-and-Spoke, systems exchange information through a central hub, reducing direct dependencies and supporting greater consistency. In Publish-Subscribe, producing systems publish data while interested consumers subscribe to the relevant data service or event stream. DMBOK2 presents these three patterns explicitly within Data Integration and Interoperability.
The choice of pattern affects Data Quality as well as architecture. Point-to-point designs can create inconsistent transformations when several applications independently consume the same source. Hub-and- spoke designs can centralize transformations and reference mappings. Publish-subscribe supports scalable distribution but requires controlled message definitions and metadata so that every subscriber interprets data consistently.
DAMA therefore recommends choosing an interaction model according to functional, performance, latency, support, and governance requirements rather than allowing integration patterns to emerge independently.
Reference Topics: DAMA-DMBOK2 Chapter 8 - Data Integration and Interoperability; Interaction Models; Point-to-Point; Hub-and-Spoke; Publish-Subscribe; Canonical Models.
NEW QUESTION # 107
An organization changes the definition of "Net Revenue". Before implementing the change, it wants to identify every report, transformation, and analytical model that depends on the existing definition. Which capability is most important?
Answer: C
Explanation:
The required capability is Metadata lineage and impact analysis. Changing a business definition can affect far more than the glossary entry itself. The organization needs to know which physical fields, transformation rules, ETL jobs, calculations, reports, dashboards, analytical models, and downstream datasets depend on the current definition.
Lineage shows where data originates and how it flows and transforms. Impact analysis follows those relationships forward to determine what will be affected by a proposed change.
This prevents a common Data Quality failure in which a definition changes in one area while downstream systems continue applying the old interpretation. The result can be technically correct calculations that are semantically inconsistent.
A governed change should therefore update the business glossary, transformation metadata, Data Quality rules, reporting definitions, and affected documentation in a coordinated manner. Appropriate Data Stewards and Data Owners should approve the new meaning and effective date.
DAMA's framework describes Metadata Management as enabling understanding through definitions, lineage, and usage across systems, while the revised Chapter 13 explicitly strengthens the Data Quality-Metadata relationship.
Reference Topics: DAMA-DMBOK2 - Metadata Management; Data Lineage; Impact Analysis; Business Glossary; Data Quality Consistency.
NEW QUESTION # 108
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