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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Quality Fundamentals | 20% | - Definition of Data Quality - Data Quality in the Data Lifecycle - Dimensions of Data Quality (completeness, accuracy, consistency, timeliness, validity) |
| Topic 2: Data Quality Improvement | 20% | - Standardization - Data Cleansing Techniques - Data Enrichment - Validation Rules |
| Topic 3: Data Quality Governance | 15% | - Data Quality Roles and Responsibilities - Policies and Procedures - Data Stewardship - Continuous Monitoring |
| Topic 4: Data Quality Frameworks and Standards | 20% | - Other Industry Standards - ISO 8000 (Data Quality) - DAMA DMBOK Framework |
| Topic 5: Data Quality Assessment and Measurement | 25% | - Profiling Techniques - Metrics and KPIs - Benchmarking - Root Cause Analysis |
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NEW QUESTION # 92
Adoption of a Data Governance program is most likely to succeed:
Answer: A
Explanation:
DAMA-DMBOK2 recommends an incremental implementation strategy for Data Governance because governance introduces changes in accountability, decision rights, standards, processes, and organizational behaviour. Attempting an enterprise-wide implementation immediately creates unnecessary cultural and operational risk.
The implementation guidance is explicit that most governance rollout strategies are incremental, commonly beginning with a significant initiative such as Master Data Management or with a particular region, business unit, or domain. Enterprise-wide deployment as the initial step is described as uncommon.
Incremental implementation provides several control advantages. The organization can validate roles and decision mechanisms, establish stewardship practices, prove business value, refine policies, develop metrics, and correct weaknesses before expanding the governance framework. This aligns closely with Data Quality management, where organizations normally prioritize Critical Data Elements and high-value domains rather than attempting to measure and remediate every data element simultaneously.
Executive sponsorship is important, but a mandate alone does not produce sustainable adoption. Likewise, individual leadership charisma cannot substitute for operating processes, accountability, metrics, and change management. A short consultant-driven exercise also conflicts with DAMA's treatment of governance as an ongoing organizational capability.
Incremental rollout therefore offers the strongest mechanism for embedding governance permanently while demonstrating measurable value.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Implementation Guidelines; Organization and Culture; Adjustment and Communication; Chapter 13 - Data Quality Improvement and Governance.
NEW QUESTION # 93
Critical to the incremental development of the data warehouse is:
Answer: B
Explanation:
A strong Release Management process is critical when a Data Warehouse evolves incrementally. DAMA- DMBOK2 states directly that Release Management supports incremental development by coordinating new capabilities, enhancements, production deployment, and recurring maintenance of deployed warehouse assets.
A Data Warehouse is rarely completed in one implementation. Business requirements evolve, additional subject areas are onboarded, models are extended, transformation rules change, new reports are introduced, and defects are corrected. Release Management provides the controlled mechanism for packaging these changes into predictable production increments.
This requires prioritization of the backlog, coordination between business and technical teams, regression testing, deployment control, documentation, reconciliation, and validation of new or changed data structures.
Without disciplined release management, incremental development can produce incompatible transformations, unstable reports, inconsistent historical treatment, and uncontrolled changes to definitions.
Data Quality should therefore form part of each release gate. New mappings and transformations should be profiled and reconciled; quality thresholds should be retested; metadata and lineage must be updated; and known exceptions should be documented.
Agile development may be used as a delivery method, but DAMA specifically identifies Release Management as the process critical to sustaining incremental warehouse evolution.
Reference Topics: DAMA-DMBOK2 Data Warehousing and Business Intelligence - Maintain Data Products; Release Management; Incremental Development; Chapter 13 - Quality Monitoring and Change Control.
NEW QUESTION # 94
One way of defining ethics is:
Answer: E
Explanation:
The correct principle is "Doing it right when no one is looking." The statement captures the essential distinction between ethical behaviour and mere compliance. Ethical conduct does not depend on observation, enforcement, or the probability of being caught; it requires responsible action because the action itself is appropriate. The answer wording is presented directly in the PDF on page 4.
DAMA-DMBOK2 treats data ethics as an essential component of professional Data Management because organizations routinely make choices about collecting, combining, analyzing, retaining, sharing, and monetizing data. A technically permissible activity may still create inappropriate effects on individuals or expose data to potential misuse.
Data ethics therefore extends beyond security and regulatory compliance. It requires consideration of the impact on people, potential for misuse, and economic value of data. Ethical handling also requires transparency concerning data provenance, intended use, quality limitations, and the consequences of decisions based on data.
Data Quality has an ethical dimension as well. Knowingly using inaccurate, incomplete, misleading, or poorly understood data in consequential decisions can harm customers and other stakeholders. Governance should therefore establish not only compliance controls but also principles for responsible use.
Reference Topics: DAMA-DMBOK2 Chapter 2 - Data Handling Ethics; Ethical Principles; Impact on People; Potential for Misuse; Chapter 13 - Reliable and Fit-for-Purpose Data.
NEW QUESTION # 95
During initial profiling of a Country_Code attribute, the analyst wants to identify unexpected values such as
"UKK", "ENG", and blanks. Which profiling output is most useful?
Answer: A
Explanation:
A frequency distribution is the most useful profiling output because it shows each distinct value and how often it occurs. For a controlled attribute such as Country_Code, this quickly reveals unexpected values, misspellings, blanks, obsolete codes, and disproportionately common defaults.
Data profiling is an analytical process used to examine actual data populations and determine their structure, content, patterns, distributions, and potential defects. Frequency analysis is particularly valuable for categorical attributes because it provides evidence about the real values present rather than relying only on declared metadata.
The result can be compared with an authoritative Reference Data domain. Values outside the approved code set become validity exceptions, while unusual frequencies may indicate mapping defects or default-value problems.
This is also where Metadata Management and Reference Data Management interact strongly with Data Quality. Metadata identifies the expected definition and domain; Reference Data provides the authorized codes; profiling determines whether operational values actually conform.
A data model can show that Country_Code exists and perhaps its datatype, but it does not reveal the actual value distribution currently stored in production.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Profiling; Frequency Analysis; Validity; Reference Data; Metadata Management.
NEW QUESTION # 96
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 # 97
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