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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Tools, Techniques and Implementation | 20% | - Business value and ROI - Data quality in lifecycle management - Data quality tools and technologies |
| Topic 2: Data Quality Assessment and Profiling | 25% | - Assessment methods and tools - Defining quality rules and metrics - Data profiling techniques |
| Topic 3: Data Quality Improvement | 20% | - Root cause analysis - Monitoring and continuous improvement - Data cleansing and standardization |
| Topic 4: Data Quality Strategy and Governance | 20% | - Roles and responsibilities - Data quality strategy and policy - Integration with data governance |
| Topic 5: Data Quality Concepts and Principles | 15% | - Definition and importance of data quality - Data quality in DMBOK framework - Data quality dimensions |
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NEW QUESTION # 24
The DMBoK 'Environmental Factors hexagon' shows the relationship between:
Answer: D
Explanation:
The DAMA-DMBOK2 Environmental Factors Hexagon illustrates the relationship between People, Process, and Technology. It provides a visual key for interpreting the Knowledge Area Context Diagrams and emphasizes that effective Data Management depends on the coordinated interaction of these three dimensions.
DMBOK2 places Goals and Principles at the center of the hexagon because they guide how people perform activities and how technology is selected and used. People-related factors include roles, responsibilities, organization, and culture. Process-related factors include activities, techniques, and methods. Technology- related factors include tools and the mechanisms that support Data Management deliverables.
This model is especially relevant to Data Quality. A profiling or cleansing platform alone cannot establish sustainable quality. The organization also needs accountable Data Owners and Data Stewards, repeatable issue-management and improvement processes, business-defined rules, and governance mechanisms.
Conversely, well-designed processes will fail if supporting technologies cannot monitor or enforce required controls.
Option A relates more closely to the structure of DMBOK Knowledge Area Context Diagrams. The DAMA Wheel represents the Knowledge Areas, while the Environmental Factors Hexagon specifically emphasizes the People-Process-Technology relationship.
Reference Topics: DAMA-DMBOK2 Chapter 1 - DAMA-DMBOK Framework; Environmental Factors Hexagon; People, Process and Technology; Chapter 13 - Data Quality Organization, Processes and Tools.
NEW QUESTION # 25
What is the purpose of Data Governance?
Answer: B
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 # 26
Which of the following is a reason why organisations do not dispose of non-value-adding information?
Answer: C
Explanation:
The correct answer is storage is cheap and easily expanded. DAMA-DMBOK2 discusses retention and disposal as important lifecycle-management responsibilities. Although information that no longer provides business, legal, regulatory, historical, or evidentiary value should normally be disposed of according to approved retention policies, organizations often postpone disposal because modern storage appears inexpensive and technically easy to expand.
This reasoning is deceptive. The acquisition cost of storage is only one component of total information- management cost. Retaining unnecessary information also increases backup requirements, recovery time, discovery obligations, privacy exposure, security risk, metadata-management effort, migration complexity, and the volume of information that must be governed. DMBOK2 therefore stresses that non-value-adding information should not be retained merely because storage capacity is readily available.
From a Data Quality perspective, excessive retention also increases the population of obsolete, redundant, and potentially inconsistent information. This makes profiling, lineage analysis, master-data reconciliation, and authoritative-source identification more difficult.
A sound governance program consequently combines retention schedules, legal requirements, metadata classification, defensible disposal procedures, and clear accountability so that data is retained for legitimate reasons rather than technological convenience.
Reference Topics: DAMA-DMBOK2 - Document and Content Management; Retention and Disposal; Information Lifecycle; Data Governance; Chapter 13 - Data Quality and Obsolete Data.
NEW QUESTION # 27
Compound authorization groups provide a means to:
Answer: D
Explanation:
Compound authorization groups allow access rights from multiple groups or roles to be combined so that an individual's effective permissions can be configured with greater precision. The user may inherit one set of privileges from an organizational role, another from a functional group, and additional permissions from a specialized operational responsibility. The resulting authorization can therefore be more granular than assigning every privilege individually.
DAMA's Data Security framework separates authentication-establishing identity-from authorization, which determines what an authenticated identity is permitted to access or perform. Group-based authorization is a practical mechanism for implementing those decisions. CDMP-oriented references for this specific item identify "precisely configure an individual's access to a system" as the intended answer.
The principle must nevertheless be governed carefully. Excessive overlapping group memberships can make effective permissions difficult to review and can create segregation-of-duties conflicts. Periodic access reviews, least-privilege design, role ownership, and audit trails are therefore necessary complementary controls.
Encryption addresses confidentiality of data in transit or storage and is unrelated to how permissions are assigned. Audit preparation may benefit from structured authorization, but it is not the principal purpose.
Reference Topics: DAMA-DMBOK2 Chapter 7 - Authorization; Access Control; Least Privilege; Security Roles; Audit and Monitoring.
NEW QUESTION # 28
A Data Quality team repeatedly corrects invalid postal codes in the warehouse, but the same errors reappear after every nightly load. What is the most appropriate long-term response?
Answer: C
Explanation:
The correct long-term response is to remove the root cause in the source or integration process. Repeated warehouse cleansing treats symptoms. If the defect is recreated every night, operational costs continue and downstream systems remain exposed until the cleansing process executes.
DAMA Data Quality practice emphasizes sustained improvement rather than repeated correction. The quality lifecycle therefore includes identifying defects, determining business impact, analyzing root causes, implementing corrective actions, and monitoring results. The current Chapter 13 revision specifically strengthens clarification of the Data Quality Improvement Lifecycle and responsibilities within it.
The underlying cause might be weak source validation, an incorrect source-to-target mapping, outdated Reference Data, transformation logic, or missing governance over postal-code rules.
Cleansing remains appropriate where historical data must be repaired or immediate downstream protection is required. However, preventive control should be introduced as close to the creation point as practical.
Metadata and lineage help trace the postal code through the data flow, while governance establishes who has authority to change the offending process.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Root-Cause Analysis; Remediation; Prevention; Cleansing; Data Integration and Lineage.
NEW QUESTION # 29
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