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| Certification Vendor: | DAMA International |
|---|---|
| Exam Name: | DAMA Data Quality (DQ) Exam |
| Exam Number: | DQ-1220 |
| Related Certifications: | Certified Data Management Professional (CDMP) |
| Exam Format: | Multiple Choice Questions |
| Available Languages: | English |
| Recommended Training: | DAMA-DMBOK Knowledge Guide |
| Exam Registration: | DAMA International Official Website |
| Sample Questions: | DAMA DQ-1220 Sample Questions |
| Exam Way: | Online proctored or authorized testing centers depending on provider |
| Pre Condition: | No strict prerequisites; familiarity with DAMA-DMBOK recommended |
| Official Syllabus URL: | https://www.dama.org |
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DAMA DQ-1220 Certification Exam is recognized globally as a standard for measuring the competency of data quality professionals. It is a comprehensive exam that assesses the candidate's understanding of the principles, techniques, and methodologies used in data quality management. DQ-1220 exam is designed to evaluate the candidate's ability to identify and resolve data quality issues, develop and implement data quality plans, and measure the effectiveness of data quality initiatives. Passing the DAMA DQ-1220 Certification Exam demonstrates that the candidate has the knowledge and skills necessary to ensure that organizational data is accurate, reliable, and trustworthy.
DAMA DQ-1220 Exam is a globally recognized certification that measures the knowledge and understanding of data quality management principles and best practices. DQ-1220 Exam is specifically designed for professionals who want to improve their data quality management skills and knowledge. The DAMA DQ-1220 Exam assesses candidates on concepts such as data governance, data profiling, and data quality assessment techniques. DQ-1220 exam covers topics such as data quality business case development, data management processes, and data auditing and monitoring.
To prepare for the DAMA DQ-1220 exam, individuals can take data quality management courses, participate in data quality management projects, and join professional organizations such as the Data Management Association (DAMA). Moreover, individuals should review and understand the DAMA DQ-1220 exam preparation guide and sample questions, ensuring that they have understood all topics in the exam.
NEW QUESTION # 90
Two departments use the term "Active Customer" but apply different definitions. Which Data Management capability should be addressed first?
Answer: D
Explanation:
The immediate requirement is Business Glossary governance. The issue is semantic: two departments use the same term but assign different meanings to it.
A governed business glossary establishes agreed terminology, definitions, synonyms, related concepts, ownership, and usage context. Resolving the definition does not necessarily mean forcing every business process to use one operational rule; there may be legitimate variants. The critical requirement is that those variants be explicitly named and documented so consumers understand the distinction.
Metadata Management provides the structures needed to preserve and distribute these definitions. DAMA's public Metadata Management guidance identifies business glossaries and data dictionaries as core mechanisms for improving shared understanding.
This semantic clarity is fundamental to Data Quality. A completeness or accuracy score for "Active Customer" is meaningless if departments measure different populations under the same label.
After the definition is governed, technical metadata should link the glossary concept to physical fields, calculations, reports, and quality rules.
Reference Topics: DAMA-DMBOK2 - Business Glossary; Metadata Management; Data Stewardship; Semantic Consistency; Data Quality Rules.
NEW QUESTION # 91
Who does the DMBoK consider to be generally responsible for developing business glossary content?
Answer: C
Explanation:
DAMA-DMBOK2 assigns primary responsibility for business glossary content to Business Data Stewards.
Business Data Stewards are typically subject-matter experts who understand how data is defined, created, interpreted, and consumed within their business domain. DMBOK2 explicitly states that Data Stewards are generally responsible for business glossary content and identifies Business Data Stewards as professionals who work with stakeholders to define and control data.
A business glossary is not simply a technical dictionary. It establishes agreed business terminology, definitions, synonyms, business rules, responsible stewards, and relationships between business concepts.
This makes stewardship involvement essential because definitions must reflect operational and business meaning rather than merely database structures.
Data Architects may contribute candidate definitions and structural context from subject-area and conceptual models, but they do not generally own the business meaning. Business users provide valuable input, while Coordinating Data Stewards help reconcile definitions across domains, yet the normal accountability remains with Business Data Stewards.
This relationship is particularly important for Data Quality. Quality rules depend on unambiguous definitions of data elements. If "Customer," "Active Account," or "Order Date" has inconsistent meanings, measurements of completeness, accuracy, and validity cannot be consistently interpreted.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Develop a Business Glossary; Business Data Stewardship; Metadata Management; Chapter 13 - Data Quality Rules and Business Definitions.
NEW QUESTION # 92
A business case for adding a new master data management solution is dependent on achieving greater value from:
Answer: E
NEW QUESTION # 93
Obfuscation or redaction of data is the practice of:
Answer: A
Explanation:
DAMA-DMBOK2 defines obfuscation or redaction as making information anonymous or removing sensitive information. The purpose is to reduce the risk that protected, confidential, or personally identifiable information can be exposed to users or processes that do not require access to the original values.
Obfuscation can involve masking, substitution, shuffling, temporal variation, partial display, or other methods that change what the recipient sees while preserving sufficient utility for the intended activity. For example, a customer-service agent may see only the final digits of an account identifier, or a development team may receive realistic but anonymized production-derived test data.
Redaction may remove information entirely from a representation when there is no legitimate requirement to expose it.
The technique is therefore a Data Security and privacy control, not database compression, publication, or classification.
There is also an important Data Quality consideration. Masked or obfuscated data used for testing must remain structurally valid and retain required relationships so that applications behave realistically. Metadata should indicate that a dataset has been transformed for privacy purposes so consumers do not mistake masked values for authoritative production data.
Reference Topics: DAMA-DMBOK2 Chapter 7 - Obfuscation; Redaction; Data Masking; Privacy; Sensitive Information; Chapter 13 - Fitness for Purpose and Controlled Transformation.
NEW QUESTION # 94
Multiple data warehouses, lakes, and swamps are an indicator that the organisation values:
Answer: A
Explanation:
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.
NEW QUESTION # 95
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