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The DAMA DQ-1220 Exam consists of 100 multiple-choice questions and you will be given 2 hours to complete it. The questions are designed to test your knowledge of data quality concepts, methodologies, and tools. You will need to have a good understanding of data profiling techniques, data quality metrics, data validation rules, data standardization methods, and data cleansing techniques.
NEW QUESTION # 65
Integrating data security with document and content management knowledge areas, guides the implementation of:
Answer: E
Explanation:
Document and Content Management focuses on information stored outside conventional relational databases, including documents, images, multimedia, email, and other semi-structured or unstructured assets. Integrating this knowledge area with Data Security therefore guides the implementation of appropriate access and authorization controls for unstructured data.
DAMA's framework treats security as a cross-cutting discipline rather than something applicable only to database tables. Organizational documents can contain personally identifiable information, intellectual property, contracts, financial records, or other confidential material and therefore require the same disciplined approach to authentication, authorization, classification, retention, and monitoring as structured data. DAMA- aligned references explicitly identify appropriate access and authorization to unstructured data as the relevant interaction between these knowledge areas.
Metadata is also important because document classifications, ownership, retention category, confidentiality level, and permitted audiences provide the information needed to enforce controls.
Option A refers to structured data and therefore misses the specific contribution of Document and Content Management. Fitness-for-purpose measurement belongs primarily to Data Quality, while data-mart privacy addresses a narrower structured analytical environment.
Reference Topics: DAMA-DMBOK2 Chapter 7 - Data Security; Chapter 9 - Document and Content Management; Unstructured Data; Access Control; Authorization; Information Classification.
NEW QUESTION # 66
A company converts "Street", "St.", and "Str" to a single approved address representation before customer matching. This is an example of:
Answer: C
Explanation:
The activity is Standardization. Standardization converts semantically equivalent values into a consistent representation so they can be compared, matched, integrated, and interpreted reliably.
In the scenario, Street, St., and Str may represent the same address concept but differ syntactically.
Converting them to an approved standard reduces superficial variation before entity matching.
Standardization is commonly used for names, addresses, telephone numbers, units of measure, dates, product descriptions, geographic references, abbreviations, and coded values. It frequently precedes matching and de- duplication because entity-resolution algorithms perform more effectively when predictable representational differences have already been normalized.
Reference Data and Metadata Management are important supporting disciplines. Reference Data can define approved abbreviations or formats, while metadata documents transformation rules and intended meaning.
Standardization does not prove that the resulting address is factually accurate. It improves consistency and comparability. An address can be perfectly standardized yet belong to the wrong customer.
Therefore, standardization should be combined with validation, verification, matching, and source- improvement controls where appropriate.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Standardization; Cleansing; Matching; Consistency; Reference Data Management.
NEW QUESTION # 67
An address-validation service reports that 96% of customer addresses conform to an approved postal format.
What does this metric demonstrate most directly?
Answer: A
Explanation:
The metric demonstrates Validity because it measures whether address values conform to a defined format or standard.
Validity checks whether data satisfies specified rules concerning datatype, pattern, permitted values, ranges, or structures. Postal formatting rules might require approved postcode structure, valid country-code syntax, or required address components.
The metric does not prove that the address belongs to the correct customer. A perfectly formatted address may still be factually wrong. That would be an Accuracy problem.
DAMA-aligned government guidance defines validity in terms of conformity to expected format, type, range, and other predefined rules.
A mature address-quality program may combine validity with verification against authoritative postal reference data, duplication checks, completeness rules, and currency monitoring.
Quality scorecards should therefore label the metric precisely rather than calling the result a generic "accuracy score." Reference Topics: DAMA-DMBOK2 Chapter 13 - Validity; Accuracy; Data Quality Metrics; Address Quality; Reference Data.
NEW QUESTION # 68
The implementation of a 'Master Data Repository', which is integrated across the enterprise, is an example of which integration approach?
Answer: B
Explanation:
An enterprise Master Data Repository serving multiple applications is a classic Hub-and-Spoke integration pattern. In this architecture, shared information is consolidated physically or virtually in a central hub, while participating applications interact with that hub rather than building a separate direct interface to every other application.
DAMA-DMBOK2 explicitly identifies Master Data Management hubs, Data Warehouses, Data Marts, and Operational Data Stores as familiar examples of data hubs. The hub-and-spoke model reduces the proliferation of point-to-point interfaces and can provide a consistent enterprise view of shared data.
This architecture is particularly appropriate for MDM because customer, product, supplier, location, or other master entities often need to be standardized, matched, governed, and redistributed across many systems. The central hub can apply survivorship rules, reference mappings, stewardship decisions, and quality controls before distributing trusted values.
Replication and Change Data Capture are techniques for moving or detecting changes in data, but neither describes the overall interaction topology. Publish-subscribe may operate alongside an MDM hub as a distribution mechanism, but the enterprise repository itself represents the hub within a hub-and-spoke architecture.
Reference Topics: DAMA-DMBOK2 Chapter 8 - Hub-and-Spoke; Integration Interaction Models; Chapter
10 - Master Data Management; Enterprise Master Data Hub; Data Quality Consistency.
NEW QUESTION # 69
Who should normally define the business meaning and acceptable quality requirements for a critical customer attribute?
Answer: A
Explanation:
A Business Data Steward, working with relevant business stakeholders and governance bodies, is the appropriate role to define or coordinate business meaning and quality expectations.
Quality requirements cannot be derived solely from database structures. The organization must understand what the attribute means, how it is used, what values are acceptable, which source is authoritative, and what consequences arise when it is wrong.
Data Stewards bridge business knowledge and formal Data Management controls. They commonly participate in maintaining glossary definitions, defining business rules, resolving issues, clarifying ownership, and establishing Data Quality expectations.
Technical specialists contribute implementation knowledge. A DBA may identify datatype constraints, while an integration specialist can implement transformations. Neither should independently determine business meaning.
DAMA's public framework describes Metadata Management as supporting definitions, lineage, and governance, while Reference and Master Data Management ensures consistency in shared core entities.
The strongest operating model therefore combines business accountability with technical execution.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Data Stewardship; Chapter 11 - Business Metadata; Chapter 13 - Data Quality Requirements; Critical Data Elements.
NEW QUESTION # 70
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