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| Certification Vendor: | DAMA International |
|---|---|
| Exam Name: | Data Quality |
| Exam Number: | DQ-1220 |
| Certificate Validity Period: | No expiration for CDMP certificate (requires continuing education/renewal credits) |
| Exam Format: | Multiple Choice |
| Exam Duration: | 60 minutes |
| Passing Score: | 70% |
| Related Certifications: | CDMP - Certified Data Management Professional |
| Real Exam Qty: | 80 |
| Available Languages: | English |
| Exam Price: | USD $400 (Full CDMP exam) |
| Sample Questions: | DAMA DQ-1220 Sample Questions |
| Exam Way: | Online proctored or Onsite testing center |
| Pre Condition: | No formal prerequisites; recommended experience in data management |
| Official Syllabus URL: | https://dama.org/certification/ |
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DAMA DQ-1220 시험은 데이터 품질 관리 분야에서 경력을 향상시키고자 하는 전문가들에게 이상적입니다. 이 시험은 데이터 품질 분석가, 데이터 품질 엔지니어, 데이터 품질 매니저 및 기타 조직 전체에서 데이터의 정확성, 완전성 및 일관성을 보장하는 책임이 있는 전문가들을 위해 설계되었습니다. 이 인증은 이 분야에서 전문성을 입증하고자 하는 개인들에게 경쟁 우위를 제공합니다.
DAMA DQ-1220 자격증 취득은 데이터 관리 분야 전문가에게 귀중한 자격증입니다. 이는 데이터 품질 관리에서 최상의 실천 방법에 대한 헌신을 나타내며, 이 분야 전문가의 지식과 기술을 평가하는 국제 표준을 제공합니다. 이 자격증은 전 세계의 고용주 및 기관에서 인정받으며, 진출 기회 확대 및 수익 증대로 이어질 수 있습니다.
질문 # 26
A source field stores weight in pounds, while the target system requires kilograms. The integration specification should contain:
정답：A
설명：
The integration specification requires a documented transformation rule defining how pounds are converted to kilograms.
Source-to-target mappings should identify source attributes, target attributes, transformation logic, units, datatypes, validation expectations, and handling of exceptions. Without this metadata, different interfaces could apply different conversion factors or rounding rules and create inconsistent downstream values.
The rule should specify the approved conversion factor, precision, rounding method, treatment of nulls, and any acceptable source ranges. Testing should confirm that the transformed values remain within defined quality thresholds.
This illustrates the relationship between Data Integration, Metadata Management, and Data Quality that DAMA's current Chapter 13 revision makes more explicit.
The source value may be completely accurate in pounds while the target value becomes inaccurate through faulty transformation. Therefore, quality responsibility extends beyond original data capture.
Lineage should retain both the source and transformation information so downstream analysts can understand how the kilogram value was derived.
Reference Topics: DAMA-DMBOK2 - Data Integration and Interoperability; Source-to-Target Mapping; Transformation; Metadata Lineage; Accuracy.
질문 # 27
The Family Name of a Person is recorded in a system. The column name is Pname. Pname is an example of:
정답：A
설명：
Pname is Metadata because it is the name assigned to a database column and therefore describes the structure in which the actual Family Name values are stored. A value such as Smith or Patel would be data; Pname is structural information describing that data.
DAMA-DMBOK2 defines Metadata broadly as information about data and data-related processes. Technical metadata includes physical database characteristics such as table names, column names, datatypes, lengths, indexes, constraints, and related structural properties. The interpretation of Pname as metadata is also consistent with the published version of this exact certification question.
The fact that Pname may be an unclear abbreviation does not change its classification. It may represent a poor naming convention from a usability or governance perspective, but technically it remains metadata. A stronger physical name might be FamilyName, while the metadata repository could additionally map that field to the authoritative business glossary term.
This distinction matters to Data Quality because rules are frequently attached to metadata objects. For example, the Family Name attribute might have completeness, permitted-character, maximum-length, and standardization requirements.
Effective Metadata Management connects the technical column to its business meaning, system lineage, stewardship, and applicable Data Quality controls.
Reference Topics: DAMA-DMBOK2 Chapter 11 - Technical Metadata; Database Metadata; Business Glossary; Chapter 13 - Data Quality Rules and Metadata.
질문 # 28
Which of the following activities is NOT a way that enterprise data architecture influences the scope boundaries of projects?
정답：E
설명：
Ensuring enterprise business processes are effectively documented is not primarily a Data Architecture mechanism for defining project scope boundaries. That responsibility belongs more directly to Business Architecture, process management, and business-analysis disciplines.
DAMA-DMBOK2 explains that Enterprise Data Architecture influences projects by defining enterprise data requirements, reviewing project data designs, determining lineage impacts, controlling unnecessary replication, enforcing Data Architecture standards, and guiding technology and renewal decisions.
Consequently, options A, B, D, and E all represent valid ways architecture can constrain or guide project scope. A design review checks that local solutions remain compatible with enterprise strategy. Standards prevent individual projects from creating incompatible structures. Replication controls limit uncontrolled proliferation of redundant data. Enterprise data requirements ensure that projects account for information needs beyond their immediate application boundaries.
Documenting business processes remains important because processes produce and consume data, but comprehensive process documentation is not itself a primary Enterprise Data Architecture scope-control activity.
From a Data Quality perspective, architectural influence prevents project-level decisions from creating duplicated authoritative sources, inconsistent definitions, undocumented lineage, or uncontrolled transformations.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Manage Enterprise Requirements within Projects; Architecture Governance; Project Scope Boundaries; Data Replication; Chapter 13 - Consistency and Lineage.
질문 # 29
What is a steward?
정답：B
설명：
In DAMA terminology, stewardship is based on the established concept of managing an asset on behalf of another party. DAMA-DMBOK2 defines the underlying term directly: "A steward is a person whose job it is to manage the property of another person." It then applies that concept specifically to data: Data Stewards manage data assets on behalf of others and in the best interests of the organization.
A Data Steward is therefore much more than a stakeholder or sponsor. Stewardship carries explicit operational accountability. Typical responsibilities include defining and clarifying business data, participating in issue resolution, supporting standards and policies, establishing quality requirements, approving metadata definitions, and ensuring governance decisions are applied within the steward's data domain.
The relationship to Data Quality is particularly important. DMBOK2 identifies Data Stewards as participants in identifying and resolving data-related issues and in ensuring governance policies are followed. They frequently establish or approve Critical Data Elements, Data Quality rules, acceptable thresholds, business definitions, and remediation priorities. Metadata Management records those definitions and rules, while Master Data Management applies stewardship decisions to shared entities such as Customer, Product, Supplier, or Location.
A stakeholder may have an interest in the data, and a sponsor may provide authority or funding, but neither term inherently includes stewardship accountability.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Types of Data Stewards; Stewardship; Data Quality Issue Management; Chapter 13 - Data Quality Governance and Responsibilities.
질문 # 30
A company requires every system to use the same ISO country-code list managed centrally. This is primarily an example of:
정답：C
설명：
This is Reference Data Management. Country codes represent a controlled set of values used to classify or contextualize other data. Managing them centrally helps ensure that all systems interpret the same codes consistently.
Without centralized governance, one system might use GB, another UK, and another proprietary numeric values. Those differences create mapping complexity, failed integrations, inaccurate aggregation, and inconsistent reporting.
Reference Data Management establishes authoritative sources, stewardship, allowed values, mappings, effective dates, distribution processes, and change control. DAMA's public framework describes Reference and Master Data Management as ensuring consistency in core shared information across organizational units.
The Data Quality dimensions most directly supported are Validity and Consistency. Validity ensures values belong to an approved domain; consistency ensures equivalent concepts are represented compatibly across systems.
Metadata should document the standard, meaning, owner, and version of each reference domain.
Reference Topics: DAMA-DMBOK2 Chapter 10 - Reference Data; Controlled Code Sets; Authoritative Sources; Chapter 13 - Validity and Consistency.
질문 # 31
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