Free DAMA DQ-1220 Practice Exam with Questions & Answers | Set: 3

The organization should continue monitoring the quality metric. A successful remediation does not guarantee that the underlying process will remain controlled indefinitely.

Systems change, source applications are upgraded, personnel and suppliers change, reference values evolve, interfaces are modified, and business rules are revised. Any of these events can cause previously corrected defects to reappear.

A mature Data Quality lifecycle therefore treats improvement as continuous rather than as a one-time cleansing exercise. DAMA's revision of Chapter 13 specifically clarifies the Data Quality Improvement Lifecycle and links it to established continuous-improvement cycles.

Monitoring should confirm that the result remains within agreed thresholds and should provide trend information so deterioration can be detected before it creates significant business impact.

The frequency of monitoring may be reduced after a process demonstrates sustained stability, but the decision should be risk-based. Critical Data Elements generally justify stronger ongoing oversight.

The rule, threshold, owner, and measurement method should remain documented as metadata.

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

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A point materially outside established control limits is a classic signal of special-cause variation. Statistical Process Control distinguishes routine variation inherent in a stable process from unusual variation introduced by an identifiable event or change.

When a Data Quality process normally produces a stable percentage of invalid records and a sudden observation lies beyond the expected statistical range, investigation should focus on what changed. Possible causes include a new source system, software deployment, changed mapping, revised business rule, unusual batch, or manual processing error.

The current DMBOK2 Chapter 13 revision retains Statistical Process Control concepts while reorganizing them into supporting material and explicitly maps Shewhart/Deming improvement stages to Data Quality processes.

The signal does not prove what caused the defect, nor does it establish that performance has improved or deteriorated permanently. It indicates that the observed variation is sufficiently unusual to justify analysis.

Quality teams should preserve the evidence, correlate the timing with system and process changes, use lineage to identify affected sources, and investigate before adjusting the process unnecessarily.

Reference Topics: DAMA-DMBOK2 Chapter 13 — Statistical Process Control; Control Charts; Common Cause; Special Cause; Continuous Improvement.

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The critical wording is “possible, but incorrect values.” This describes values that satisfy basic syntactic or domain validation—they look like legitimate dates—but do not accurately represent the real-world attribute defined by the field.

If two systems contribute data and one maps marriage date into the birth-date field, the resulting values can be perfectly valid calendar dates while being semantically incorrect as birth dates. This is principally an Accuracy defect, because DAMA defines accuracy in terms of how correctly data represents the real-world object or event it is intended to describe. It may also expose a consistency and integration-mapping problem between source systems. DAMA's quality framework distinguishes accuracy from completeness: data can be populated and formally valid while still being factually wrong.

Missing values would primarily produce a Completeness defect rather than populated-but-incorrect values. Two correctly mapped systems would not inherently explain the problem. An offset or technical date representation could create transformation problems, but the scenario most directly illustrates semantic mis-mapping between data elements.

The appropriate remediation is therefore not simple cleansing alone. Metadata mappings, source-to-target specifications, lineage, business definitions, and integration rules should be corrected at the root cause.

Reference Topics: DAMA-DMBOK2 Chapter 13 — Accuracy, Completeness and Consistency; Root-Cause Remediation; Data Profiling; Metadata Management; Data Integration and Interoperability.

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A meaningful Data Quality metric begins with the business requirement and intended use. Quality is fundamentally contextual: data is considered fit for purpose only relative to the activity, decision, report, or process that depends on it.

For example, a delivery address used for same-day logistics may require stricter timeliness and completeness thresholds than an address retained solely for historical analysis. Without understanding the business use, a numerical quality target becomes arbitrary.

The business requirement should identify what failure matters, which dimension applies, how quality will be measured, the acceptable threshold, who owns the requirement, and what response is expected when the threshold is missed.

DAMA-aligned public guidance follows this same logic by recommending that quality rules and dimensions be prioritized according to user needs and purpose.

Technology selection comes later. Profiling tools help measure data, but they cannot determine the business meaning of an acceptable result.

Governance and stewardship should approve the requirement, while Metadata Management should preserve the definition, rule, owner, and lineage.

Reference Topics: DAMA-DMBOK2 Chapter 13 — Define Data Quality Requirements; Fitness for Purpose; Metrics; Thresholds; Business Rules.

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Integrated strategic reviews provide the strongest mechanism for managing a Data Architecture roadmap because the roadmap must remain synchronized with enterprise strategy, business capability priorities, other architecture domains, projects, resources, and changing dependencies.

DAMA-DMBOK2 describes the Enterprise Data Architecture roadmap as the three-to-five-year path by which the target architecture becomes reality. Crucially, it states that this roadmap must be integrated into the overall Enterprise Architecture roadmap, including milestones, required resources, cost estimates, and business-capability workstreams. The roadmap should also reflect business requirements, current conditions, technical assessments, and organizational maturity.

This means roadmap management cannot sensibly be reduced to a once-a-year exercise. Strategic conditions, technology decisions, project sequencing, dependencies, and regulatory requirements may change throughout the roadmap period. Integrated reviews allow those changes to be evaluated in the context of the wider enterprise architecture rather than independently.

Senior-management support is necessary for authority and funding, while peer reviews and results evaluation are useful control activities. None, however, provides the same integrated strategic mechanism for keeping architectural direction aligned with enterprise priorities.

From a Data Quality perspective, such reviews also ensure that architecture changes preserve authoritative sources, lineage, integration controls, and enterprise quality requirements.

Reference Topics: DAMA-DMBOK2 Chapter 4 — Develop a Roadmap; Enterprise Architecture Integration; Data Dependencies; Lifecycle Reviews; Architecture Governance.

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A proliferation of independently implemented data warehouses, lakes, and poorly governed “data swamps” commonly indicates that the organization has emphasized technology acquisition over establishing an integrated, authoritative view of enterprise data. The issue is not the existence of multiple technologies by itself; distributed platforms can be justified by legitimate architectural requirements. The warning sign is uncontrolled duplication without clear authoritative sources, enterprise architecture, governance, lineage, and consistency controls.

DAMA-DMBOK2 treats Data Architecture as the mechanism for aligning data assets with enterprise strategy and reducing unnecessary fragmentation. A mature architecture identifies authoritative sources, data flows, integration patterns, shared definitions, and the required relationship between operational and analytical stores. The DAMA framework also emphasizes that technologies should support business and data-management objectives rather than dictate them.

From a Data Quality perspective, uncontrolled proliferation creates competing versions of values, inconsistent transformations, duplicate master records, contradictory metrics, and unclear ownership. These conditions undermine consistency and make root-cause analysis difficult.

A “single point of truth” does not necessarily require one physical database. It requires controlled authority and consistent semantics across the landscape.

Reference Topics: DAMA-DMBOK2 — Data Architecture; Data as an Asset; Authoritative Sources; Data Integration; Metadata; Data Quality Consistency.

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The principal risk is inconsistent customer-name values across systems. When multiple applications can independently update the same business fact without an authoritative source or coordinated Master Data process, conflicting representations are likely to emerge.

The problem is architectural and governance-related as much as it is a Data Quality issue. The organization must determine where the authoritative value is created, who is responsible for approving changes, how updates propagate, and how conflicts are resolved.

DAMA's public framework emphasizes a single trusted or consistent approach to shared core entities through Reference and Master Data Management.

The relevant Data Quality dimensions include Consistency, Accuracy, Currency, and potentially Uniqueness. An application may hold a technically valid legal name but still contain an outdated or conflicting value.

Metadata and lineage should document which systems create, master, distribute, or merely consume the attribute.

Reference Topics: DAMA-DMBOK2 — Authoritative Sources; Master Data Management; Data Architecture; Consistency; Lineage; Stewardship.

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DAMA-DMBOK2 establishes the guiding principle that Big Data management requires more discipline than relational data management. The reason is not simply data volume. Big Data environments combine large volumes with substantial variation in structure, source, velocity, semantics, and reliability. These characteristics increase the probability of uncontrolled duplication, inconsistent interpretation, poor provenance, inappropriate use, and undetected quality defects. DAMA's Big Data guidance specifically emphasizes disciplined management of metadata describing Big Data sources, their origins, and their value.

From a Data Quality perspective, scale does not make data fit for purpose. Large datasets still require defined quality expectations, profiling, monitoring, lineage, controlled transformations, ownership, and governance. Metadata becomes particularly important because analysts must understand where datasets originated, how they were transformed, their meaning, and whether they are appropriate for a particular analytical objective.

Data Governance must consequently establish accountability, acceptable-use policies, security requirements, quality expectations, and escalation mechanisms. Statistical and automated controls are also essential because manual inspection becomes impractical at Big Data scale.

Neither larger budgets nor data-science certification substitutes for sound data-management discipline. Likewise, reducing controls because data is large creates greater—not lower—operational and analytical risk.

Reference Topics: DAMA-DMBOK2 — Big Data and Data Science; Big Data Management Principles; Metadata Management; Data Governance; Chapter 13 — Fitness for Purpose and Data Quality Controls.

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The correct definition is the fundamental organisation of a system, and the principles governing its design and evolution. DAMA-DMBOK2 incorporates the ISO/IEC 42010 architectural concept when establishing the theoretical basis for Data Architecture.

The fuller ISO formulation describes architecture in terms of the fundamental organization of a system embodied in its components, their relationships to one another and to the environment, together with the principles governing design and evolution. DAMA uses this concept to distinguish true architecture from a collection of diagrams or implementation specifications.

Option E is therefore incomplete: architectural artifacts document architecture, but the artifacts themselves are not the architecture. Option C focuses too narrowly on implementation and maintenance, while options B and D describe potential objectives or constraints rather than the definition.

Applied to enterprise data, this means architecture establishes the high-level organization of data assets, information flows, platforms, relationships, and guiding principles by which the data environment evolves.

Data Quality benefits because architectural principles establish where authoritative information originates, how it moves, how duplication is controlled, and where governance and quality controls must operate.

Reference Topics: DAMA-DMBOK2 Chapter 4 — Architecture Definition; ISO/IEC 42010; Enterprise Data Architecture; Architectural Principles; Chapter 13 — Quality by Design.

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DAMA-DMBOK2 distinguishes descriptive, structural, and administrative metadata when discussing metadata classifications used outside conventional IT contexts. Descriptive metadata identifies and describes a resource sufficiently to enable discovery and retrieval. DMBOK2 gives title, author, and subject as explicit examples of descriptive metadata.

A book's author, title, and subject therefore answer practical discovery questions: What is the resource? Who created it? What is it about? These attributes allow catalogues, repositories, search tools, and users to identify and retrieve the required resource.

Option A is principally structural metadata. DMBOK2 specifically identifies the number of pages and chapters as examples describing relationships within or among the components of a resource. Option D represents administrative metadata because version numbers and archive dates help manage the resource throughout its lifecycle.

The distinction is important to Data Quality because metadata itself must have appropriate completeness, consistency, currency, and accuracy. Incorrect descriptive metadata can make a valid data asset effectively undiscoverable or lead users to select an inappropriate source.

Metadata Management therefore supports Data Quality by giving data consumers reliable definitions, context, provenance, ownership, lineage, and usage information.

Reference Topics: DAMA-DMBOK2 Metadata Management — Descriptive Metadata; Structural Metadata; Administrative Metadata; Metadata Quality; Chapter 13 — Data Quality and Metadata Management.

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