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| Certification Vendor: | DAMA International |
|---|---|
| Exam Name: | DAMA Data Quality (DQ) Exam |
| Exam Number: | DQ-1220 |
| Exam Format: | Multiple Choice Questions |
| Related Certifications: | Certified Data Management Professional (CDMP) |
| 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 Exam is ideal for professionals who are looking to advance their careers in the field of data quality management. DQ-1220 exam is designed for data quality analysts, data quality engineers, data quality managers, and other professionals who are responsible for ensuring the accuracy, completeness, and consistency of data across an organization. Data Quality certification provides a competitive advantage to individuals seeking to demonstrate their expertise in this field.
NEW QUESTION # 27
Two departments use the term "Active Customer" but apply different definitions. Which Data Management capability should be addressed first?
Answer: A
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 # 28
Three source systems provide different telephone numbers for the same customer. An MDM hub selects one number according to approved source-priority rules. This is an example of:
Answer: C
Explanation:
The process is Survivorship. In Master Data Management, survivorship determines which value should become the preferred or authoritative representation when multiple source records contain conflicting values for the same attribute.
Rules may prioritize particular systems, use recency, trust scores, verification status, completeness, or combinations of factors. For example, a verified customer-service update might outrank an older marketing- system telephone number.
Survivorship occurs after or in conjunction with matching and entity resolution. The organization first determines that records from different sources represent the same real-world customer and then determines which attribute values should populate the mastered representation.
The process requires governance because the "best" value is a business decision, not merely a technical one.
Data Stewards should approve the rules, and metadata should record source priority, rule logic, and lineage.
DAMA's framework positions Reference and Master Data Management as the discipline responsible for ensuring consistent core entities across the organization.
Reference Topics: DAMA-DMBOK2 Chapter 10 - Master Data Management; Matching; Survivorship; Authoritative Values; Chapter 13 - Consistency and Accuracy.
NEW QUESTION # 29
Descriptive metadata describes a resource, enabling its identification and retrieval.
Answer: C
Explanation:
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.
NEW QUESTION # 30
Examples of transformation include:
Answer: D
Explanation:
Data transformation includes operations such as format changes, structural changes, semantic conversion, de- duplication, and re-ordering. These activities modify source information so that it conforms to the syntactic, structural, or semantic requirements of a target environment.
A format transformation may convert dates from DD/MM/YYYY to an ISO representation. Structural transformation may split, combine, flatten, or restructure attributes. Semantic conversion changes representation while preserving intended meaning-for example, translating a source status code into the standardized enterprise code. De-duplication identifies multiple records representing the same real-world entity, while re-ordering changes the sequence or organization of records or attributes. The listed combination aligns directly with DAMA-oriented transformation guidance.
The distinction from the distractors is important. Organizational change, infrastructure replacement, or application modernization may trigger data transformation, but they are not themselves data-transformation techniques. "Re-duplication" is also inconsistent with the objective of improving integrated datasets.
Transformation is strongly connected to Data Quality. Poorly specified conversion rules can create invalid values, truncate data, introduce semantic inconsistencies, or produce duplicate entities. Consequently, transformations should be documented through mappings and metadata, tested against quality rules, reconciled with source totals, and monitored for exceptions.
Reference Topics: DAMA-DMBOK2 Chapter 8 - Transformation and Mapping; ETL/ELT; Chapter 13 - Standardization, Cleansing, De-duplication and Validation.
NEW QUESTION # 31
A business case for adding a new master data management solution is dependent on achieving greater value from:
Answer: A
NEW QUESTION # 32
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