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DAMA DQ-1220 (Data Quality) Certification Exam is designed to test individuals’ knowledge and understanding of data quality principles and best practices. DQ-1220 exam is developed by the Data Management Association International (DAMA), a global organization that provides education, certification, and networking opportunities to data management professionals. The DAMA DQ-1220 exam aims to certify individuals who can demonstrate their expertise in data quality management and contribute to the overall improvement of data quality in their organizations.
DAMA DQ-1220 (Data Quality) Exam is a certification test specifically designed to measure an individual's knowledge and skills in the field of data quality. DQ-1220 exam is primarily intended for data management professionals who are responsible for ensuring the accuracy, completeness, and consistency of data. Passing DQ-1220 exam is an excellent way to demonstrate your expertise in data quality and to stand out in a competitive job market.
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DAMA DQ-1220 Exam is ideal for individuals who are responsible for managing the quality of data within their organizations. This could include data analysts, data stewards, data architects, and data quality managers. By earning this certification, professionals can demonstrate their expertise in ensuring that data is accurate, complete, consistent, and timely.
NEW QUESTION # 77
The main difference between a System of Record and a System of Reference is:
Answer: E
Explanation:
DAMA-DMBOK2 distinguishes the two concepts according to origination versus authoritative consumption.
A System of Record (SoR) is an authoritative system in which data is created, captured, or maintained according to defined rules and expectations. A System of Reference (SoRef) is an authoritative location from which consumers obtain reliable information for transactions or analysis even though that information may have originated elsewhere.
Therefore, option A expresses the essential distinction most accurately. For example, an operational CRM may be the System of Record for customer data because customer information is captured and maintained there. An MDM hub or enterprise data-sharing platform can subsequently become the System of Reference used by other applications, even though the original data was created in the CRM.
This distinction is important for Data Quality and lineage. Consumers need to know where values originated, where authoritative corrections are performed, which system distributes trusted values, and which stewardship process governs conflicts between sources. Metadata should therefore record source lineage, system roles, transformations, and authoritative status.
Option D is incorrect because "System of Record" and "System of Reference" do not correspond respectively to master data and reference data categories. Either architectural role can participate in managing different categories of enterprise data.
Reference Topics: DAMA-DMBOK2 Chapter 10 - System of Record and System of Reference; Trusted Sources; MDM Architecture; Metadata Lineage; Chapter 13 - Data Quality and Reference/Master Data Management.
NEW QUESTION # 78
Critical to the incremental development of the data warehouse is:
Answer: C
Explanation:
A strong Release Management process is critical when a Data Warehouse evolves incrementally. DAMA- DMBOK2 states directly that Release Management supports incremental development by coordinating new capabilities, enhancements, production deployment, and recurring maintenance of deployed warehouse assets.
A Data Warehouse is rarely completed in one implementation. Business requirements evolve, additional subject areas are onboarded, models are extended, transformation rules change, new reports are introduced, and defects are corrected. Release Management provides the controlled mechanism for packaging these changes into predictable production increments.
This requires prioritization of the backlog, coordination between business and technical teams, regression testing, deployment control, documentation, reconciliation, and validation of new or changed data structures.
Without disciplined release management, incremental development can produce incompatible transformations, unstable reports, inconsistent historical treatment, and uncontrolled changes to definitions.
Data Quality should therefore form part of each release gate. New mappings and transformations should be profiled and reconciled; quality thresholds should be retested; metadata and lineage must be updated; and known exceptions should be documented.
Agile development may be used as a delivery method, but DAMA specifically identifies Release Management as the process critical to sustaining incremental warehouse evolution.
Reference Topics: DAMA-DMBOK2 Data Warehousing and Business Intelligence - Maintain Data Products; Release Management; Incremental Development; Chapter 13 - Quality Monitoring and Change Control.
NEW QUESTION # 79
The Family Name of a Person is recorded in a system. The column name is Pname. Pname is an example of:
Answer: E
Explanation:
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.
NEW QUESTION # 80
In Data Modelling, the generalization of the concept of person and organisation into a party enables:
Answer: A
Explanation:
The Party pattern generalizes Person and Organization under a common supertype so that both can participate consistently in shared business relationships and processes. The major benefit is that persons and organizations can act in roles in agreements, share address structures, and participate in common interaction processes, making option E correct.
For example, a Customer, Supplier, Account Holder, Employee, or Contracting Party may be either a person or an organization. Without the Party abstraction, separate relationships may need to be constructed repeatedly for Person and Organization. With Party, common concepts such as Agreement, Address, Contact Point, Role, and Interaction can reference a single generalized entity.
This is particularly relevant to Master Data Management because Party is a common enterprise modelling pattern for Customer, Supplier, and related master-data domains. It supports entity resolution while preserving distinctions between individuals and organizations through subtype structures.
From a Data Quality perspective, the approach improves consistency by reducing duplicated relationship definitions and business rules. However, governance must still establish subtype-specific requirements-for example, organization registration identifiers versus personal names and dates of birth.
The abstraction is therefore primarily about reusable semantic relationships and roles, not security concealment or faster package implementation.
Reference Topics: DAMA-DMBOK2 Chapter 5 - Data Modeling and Design; Generalization and Specialization; Party Model; Chapter 10 - Master Data; Chapter 13 - Consistency and Integrity.
NEW QUESTION # 81
A pensioner who usually receives a quarterly bill of around $300 was sent a $100,000,000 electricity bill.
They were a victim of poor data quality checks in which dimension?
Answer: E
Explanation:
The failed control is principally Reasonableness. DAMA treats reasonableness as the extent to which data values or patterns conform to credible expectations. The current DMBOK2 revision recognizes Reasonableness among the standard Data Quality dimensions, alongside Accuracy, Validity, Completeness, Integrity, Uniqueness, Timeliness, Currency and Consistency.
A $100,000,000 residential electricity bill may be syntactically valid: the field may accept the number, the record may satisfy referential constraints, and the value may have arrived on time. Nevertheless, it is grossly inconsistent with the customer's historical pattern of approximately $300 per quarter. A reasonableness control would therefore compare the amount with expected ranges, historical consumption, statistical limits, peer-group distributions, or business thresholds and flag the value before billing.
This illustrates an important distinction from Accuracy. Accuracy asks whether the stored value correctly represents reality. Reasonableness asks whether the value is credible in context. The absurd magnitude itself is detectable without first obtaining an independently verified "true" bill value.
Practical controls include range checks, deviation-from-history rules, z-score/outlier detection, control limits, and exception thresholds. Such rules should be governed as metadata and owned by an appropriate Data Steward.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Quality Dimensions; Reasonableness; Measurement and Monitoring; Statistical Process Control; Data Quality Rules.
NEW QUESTION # 82
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