DAMA DQ-1220 Exam Dumps - Actual Questions Answers

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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DAMA-DMBOK2 distinguishes the two concepts according to origination versus authoritative consumption. A System of Record (SoR) is an authoritative system in which data is created, captured, or maintained according to defined rules and expectations. A System of Reference (SoRef) is an authoritative location from which consumers obtain reliable information for transactions or analysis even though that information may have originated elsewhere.

Therefore, option A expresses the essential distinction most accurately. For example, an operational CRM may be the System of Record for customer data because customer information is captured and maintained there. An MDM hub or enterprise data-sharing platform can subsequently become the System of Reference used by other applications, even though the original data was created in the CRM.

This distinction is important for Data Quality and lineage. Consumers need to know where values originated, where authoritative corrections are performed, which system distributes trusted values, and which stewardship process governs conflicts between sources. Metadata should therefore record source lineage, system roles, transformations, and authoritative status.

Option D is incorrect because “System of Record” and “System of Reference” do not correspond respectively to master data and reference data categories. Either architectural role can participate in managing different categories of enterprise data.

Reference Topics: DAMA-DMBOK2 Chapter 10 — System of Record and System of Reference; Trusted Sources; MDM Architecture; Metadata Lineage; Chapter 13 — Data Quality and Reference/Master Data Management.

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Periodic archiving is critical for maintaining database performance. As a production CRM accumulates historical transactions, active tables and indexes can become increasingly large. This increases storage consumption, backup duration, index-maintenance overhead, and the quantity of data that database engines must process during operational queries.

DAMA-DMBOK2 treats archiving as an important Data Storage and Operations activity. Historical information that remains subject to retention requirements but is no longer frequently needed for operational processing can be moved to suitable archival storage. DAMA-aligned guidance for this scenario specifically links periodic transaction archiving with maintaining production database performance.

Archiving is not the same as arbitrary deletion. Retention policies, legal obligations, recovery requirements, auditability, and business value determine how long data must remain accessible and where it should be stored. The archive must also be recoverable and appropriately secured.

Data Quality implications include maintaining integrity and traceability during migration to the archive. Records should remain complete, relationships should be preserved, and metadata should indicate retention status and archival location.

Providing reporting sources or managing deleted customers may be secondary considerations, but neither is the principal purpose described in the question.

Reference Topics: DAMA-DMBOK2 Chapter 6 — Data Storage and Operations; Archiving; Database Performance; Retention; Chapter 13 — Integrity and Historical Data.

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