DAMA Updated DQ-1220 Exam Questions and Answers by aarya

The 98.5% value is a measured Data Quality result, and because it is below the approved 99.5% threshold, it represents a threshold breach.

A Data Quality metric quantifies observed performance. A threshold establishes the level of performance the business has agreed is acceptable. These should not be confused. The metric answers how are we performing?; the threshold answers is that performance acceptable?

The 98.5% completeness result says nothing about whether the populated email addresses are accurate. A field can achieve high completeness while containing incorrect or invalid values. Other rules may therefore be required for email syntax, uniqueness, deliverability, or factual correctness.

A threshold breach should trigger a predefined response appropriate to the criticality of the data. This may involve investigation, escalation, remediation, trend review, or root-cause analysis.

Metrics, calculation logic, measurement population, thresholds, ownership, and escalation rules should be documented as metadata so dashboard results remain interpretable and reproducible.

DAMA's framework explicitly places metrics, error detection, cleansing, and quality management within Data Quality Management.

Reference Topics: DAMA-DMBOK2 Chapter 13 — Data Quality Metrics; Thresholds; Scorecards; Completeness; Monitoring.

===============

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.

===============

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.

===============

DAMA-DMBOK2 defines Data Governance as the exercise of authority and control over the management of data assets. This definition distinguishes governance from the operational execution of Data Management. Independent literature describing DAMA's framework uses the same core formulation, and the DAMA Wheel places Data Governance across the other knowledge areas through this authority-and-control function.

Governance establishes decision rights, accountability, policies, standards, stewardship, issue escalation, compliance expectations, and mechanisms for monitoring whether data is being managed appropriately. It determines who is authorized to make decisions and what rules must be followed; individual Data Management knowledge areas then execute the corresponding operational activities.

Option E is closer to the broad definition of Data Management, because it refers to planning, implementation, and control across the information lifecycle. Option A describes an assessment or maturity activity. Option B describes selected governance benefits rather than its definition, while option C characterizes a collection of functions.

For Data Quality, governance provides the authority needed to define critical data, approve quality requirements and thresholds, assign accountability, prioritize remediation, and enforce standards. Without this authority structure, profiling may identify defects but the organization may lack the decision mechanisms required to correct their underlying causes.

Reference Topics: DAMA-DMBOK2 Chapter 3 — Data Governance Definition; Authority and Control; Policies and Standards; Chapter 13 — Data Quality Governance.

===============