DAMA DQ-1220 Practice Test Questions Answers

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.

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Critical Data Elements are identified so that organizations can concentrate governance and Data Quality effort where failure would create the greatest business impact.

Not every attribute deserves the same level of profiling, monitoring, stewardship, lineage documentation, control, and remediation. Applying identical controls to every field would usually be economically inefficient and may divert resources away from information that supports regulation, financial reporting, key operations, customer outcomes, or strategic decisions.

The DMBOK2 maintenance revision explicitly adds and clarifies the concept of a Critical Data Element within Chapter 13.

Once a CDE is identified, the organization can define applicable quality dimensions, measurable rules, thresholds, ownership, authoritative sources, lineage, issue escalation, and monitoring frequency.

Criticality is contextual. An attribute may be critical to one process and relatively insignificant to another. The decision should therefore reflect documented business use and risk rather than technical prominence.

Metadata remains essential because it links the CDE to definitions, systems, lineage, owners, and quality controls.

Reference Topics: DAMA-DMBOK2 Chapter 13 — Critical Data Elements; Prioritization; Business Impact; Risk; Data Governance; Metadata.

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Loading standardized country codes into a CRM is a classic example of Reference Data Integration. Country codes constitute controlled reference values used to classify or characterize other data. They differ from transactional information such as orders and payments and from master entities such as individual customers or products.

DAMA treats countries, currencies, geographic classifications, status codes, and similar controlled domains as typical Reference Data. The uploaded question therefore tests the distinction between integrating reusable code sets and integrating Master or Transaction Data. Published versions of the same DAMA item confirm Reference Data Integration as the intended answer.

In practice, an authoritative reference source may provide standardized country identifiers to CRM, ERP, MDM, analytical, and integration systems. Central management prevents different applications from independently inventing or changing equivalent codes.

This is also a Data Quality control. Reference integration improves Validity by restricting values to approved domains and Consistency by ensuring that different systems interpret equivalent countries identically. For example, an uncontrolled mixture of GB, UK, 826, and proprietary internal codes may create integration and reporting errors unless mappings are formally governed.

Metadata should document the authoritative source, code meaning, effective dates, mappings, ownership, and applicable standards.

Reference Topics: DAMA-DMBOK2 Chapter 10 — Reference Data; Code Sets; Reference Data Integration; Authoritative Sources; Chapter 13 — Validity and Consistency.

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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.

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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.

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