DQ-1220 Exam Dumps - DAMA Data Quality Specialist

DAMA-DMBOK2 explicitly identifies a core goal of Reference and Master Data Management as ensuring that shared Master and Reference Data is complete, consistent, current, and authoritative across organizational processes.

Complete means required shared entities and attributes are sufficiently populated for their intended use. Consistent means equivalent data has compatible meaning and representation wherever it is consumed. Current means the information reflects an acceptably recent state. Authoritative means the organization recognizes a trusted source or governed process for determining the accepted value.

These characteristics are particularly important because Master and Reference Data is reused widely. An incorrect Product classification, Customer identifier, Country code, or Supplier status can therefore propagate defects across many systems and business processes.

DAMA also emphasizes that shared Master and Reference Data belongs to the organization rather than to a single application or department. This creates a strong requirement for enterprise stewardship and governance.

Reference and Master Data Management consequently interacts directly with Chapter 13. MDM can consolidate and distribute shared data, but it does not guarantee quality automatically. Matching, survivorship, validation, standardization, stewardship, and continuous monitoring are required to ensure the resulting records remain trustworthy.

Reference Topics: DAMA-DMBOK2 Chapter 10 — Reference and Master Data Management Goals; Shared Data; Authoritative Sources; Stewardship; Chapter 13 — Completeness, Consistency and Currency.

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DAMA-DMBOK2 states three principal goals for Data Storage and Operations: managing data availability throughout the lifecycle, ensuring the integrity of data assets, and managing the performance of data transactions. Option A corresponds directly to those goals.

Availability means that required data and database services are accessible when business processes need them. This requires capacity planning, backup and recovery, business continuity, monitoring, and resilient infrastructure.

Integrity means protecting data assets from corruption or inappropriate alteration and maintaining reliable technical structures. Operational controls such as recovery procedures, transaction management, and controlled changes contribute directly to integrity.

Performance concerns the efficiency and responsiveness of database transactions and related storage operations. Capacity, cache behaviour, indexing, workload patterns, and system resources must therefore be monitored.

Glossary management belongs primarily to Metadata Management, while authorized access is principally a Data Security responsibility. User experience is not one of the stated core objectives for this knowledge area.

The relationship to Data Quality is direct: technically corrupted or unavailable information cannot be fit for purpose even when its business definitions are correct.

Reference Topics: DAMA-DMBOK2 Chapter 6 — Goals and Principles of Data Storage and Operations; Availability; Integrity; Performance; Chapter 13 — Data Reliability and Timeliness.

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The primary failure is Accuracy. Accuracy concerns whether a stored value correctly represents the real-world entity, event, or fact it is intended to describe. The recorded date is syntactically acceptable and conforms to the expected datatype and format, so it may satisfy Validity while still being factually wrong.

This distinction is fundamental in Data Quality assessment. A validity rule can determine whether a value is structurally permissible—for example, whether a date conforms to an approved pattern or whether a month lies between 1 and 12. It cannot by itself prove that the date belongs to the correct person. Government guidance based on DAMA dimensions makes the same distinction: a value can be valid while remaining inaccurate.

The strongest accuracy control would compare the value with an authoritative or independently verified source. Metadata should document the authoritative source and lineage, while governance should assign responsibility for resolving discrepancies.

The scenario therefore demonstrates why Data Quality programs must avoid assuming that format compliance equals correctness.

Reference Topics: DAMA-DMBOK2 Chapter 13 — Accuracy; Validity; Authoritative Sources; Data Quality Rules; Metadata Lineage.

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Predictive analytics applies statistical, mathematical or machine-learning models to historical and current data in order to estimate future events, behaviours or outcomes. DAMA's treatment of analytics positions predictive analytics as a use of managed data in which patterns discovered from existing information are used to project what is likely to occur rather than merely describe what has already occurred. DMBOK2 explicitly places predictive analytics among advanced uses of data and notes the evolution of Business Intelligence from retrospective analysis toward predictive methods.

Probability estimation is therefore the distinguishing feature in this question. Descriptive analytics summarizes historical conditions—what happened. Predictive analytics estimates what may happen and commonly produces probabilities, scores, classifications or forecasts. “Reactive analytics,” “dimensional analytics,” and “just-in-time reporting” do not describe this statistical forecasting function.

Data Quality is fundamental to predictive performance. Missing values, inaccurate labels, duplicate entities, inconsistent categories, stale observations and biased source populations can materially distort estimated probabilities. Profiling should therefore assess distributions, completeness, outliers and consistency before modelling. Metadata must preserve definitions, lineage and transformation logic, while governed Master and Reference Data provide consistent classifications across training and operational datasets.

Predictive outputs themselves can also be monitored statistically for unexpected distribution shifts and unreasonable patterns.

Reference Topics: DAMA-DMBOK2 — Data Warehousing and Business Intelligence; Predictive Analytics; Chapter 13 — Profiling, Statistical Quality Control, Reasonableness, Accuracy and Completeness.

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