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DAMA DQ-1220 Exam is a highly respected certification program for professionals in the data management field. Data Quality certification validates an individual's expertise in data quality management and demonstrates their commitment to maintaining high standards of data quality. Data Quality certification provides a competitive advantage to individuals seeking to advance their careers in this field and is highly recommended for professionals who are looking to enhance their skills and knowledge in data quality management.
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DAMA DQ-1220 (Data Quality) Certification Exam is a globally recognized certification that validates a professional’s knowledge and skills in the field of data quality. DQ-1220 exam is designed to test a candidate’s understanding of the key concepts, principles, and practices related to data quality management. It covers a broad range of topics, from data profiling and cleansing to data governance and quality measurement.
NEW QUESTION # 11
The goals of data storage and operations are:
Answer: E
Explanation:
DAMA-DMBOK2 states three principal goals for Data Storage and Operations: managing data availability throughout the lifecycle, ensuring the integrity of data assets, and managing the performance of data transactions. Option A corresponds directly to those goals.
Availability means that required data and database services are accessible when business processes need them. This requires capacity planning, backup and recovery, business continuity, monitoring, and resilient infrastructure.
Integrity means protecting data assets from corruption or inappropriate alteration and maintaining reliable technical structures. Operational controls such as recovery procedures, transaction management, and controlled changes contribute directly to integrity.
Performance concerns the efficiency and responsiveness of database transactions and related storage operations. Capacity, cache behaviour, indexing, workload patterns, and system resources must therefore be monitored.
Glossary management belongs primarily to Metadata Management, while authorized access is principally a Data Security responsibility. User experience is not one of the stated core objectives for this knowledge area.
The relationship to Data Quality is direct: technically corrupted or unavailable information cannot be fit for purpose even when its business definitions are correct.
Reference Topics: DAMA-DMBOK2 Chapter 6 - Goals and Principles of Data Storage and Operations; Availability; Integrity; Performance; Chapter 13 - Data Reliability and Timeliness.
NEW QUESTION # 12
A customer's date of birth is stored as 12/04/1980. The value conforms to the database datatype and accepted date format, but the customer's verified date of birth is 21/04/1980. Which Data Quality dimension is primarily violated?
Answer: D
Explanation:
The primary failure is Accuracy. Accuracy concerns whether a stored value correctly represents the real- world entity, event, or fact it is intended to describe. The recorded date is syntactically acceptable and conforms to the expected datatype and format, so it may satisfy Validity while still being factually wrong.
This distinction is fundamental in Data Quality assessment. A validity rule can determine whether a value is structurally permissible-for example, whether a date conforms to an approved pattern or whether a month lies between 1 and 12. It cannot by itself prove that the date belongs to the correct person. Government guidance based on DAMA dimensions makes the same distinction: a value can be valid while remaining inaccurate.
The strongest accuracy control would compare the value with an authoritative or independently verified source. Metadata should document the authoritative source and lineage, while governance should assign responsibility for resolving discrepancies.
The scenario therefore demonstrates why Data Quality programs must avoid assuming that format compliance equals correctness.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Accuracy; Validity; Authoritative Sources; Data Quality Rules; Metadata Lineage.
NEW QUESTION # 13
An MDM hub receives three customer records that have been confirmed to represent the same person. Before creating the mastered representation, what process determines which source values should populate each attribute?
Answer: A
Explanation:
The process is Survivorship. After records have been matched and determined to represent the same entity, survivorship rules determine which attribute values should populate the mastered or preferred representation.
For example, the organization may select the most recently verified telephone number, prioritize a government-verified legal name, or prefer addresses originating from a designated Customer Relationship Management system. Different attributes can use different survivorship rules.
Survivorship is not simply "choose the newest value." The appropriate method may consider source trust, verification status, recency, completeness, confidence scores, stewardship decisions, or combinations of these factors.
The rules must be governed because they determine what the organization subsequently treats as trusted master data. Metadata should record the rule, selected source, lineage, and effective date so the mastered record remains explainable.
DAMA identifies Reference and Master Data Management as responsible for maintaining consistency in core entities such as customers, products, and locations and for supporting trusted shared information.
The Data Quality dimensions involved commonly include Accuracy, Consistency, Currency, Completeness, and Uniqueness.
Reference Topics: DAMA-DMBOK2 Chapter 10 - Master Data Management; Matching; Survivorship; Golden Record; Chapter 13 - Consistency and Accuracy.
NEW QUESTION # 14
An Orders table contains an order whose Customer_ID does not exist in the Customer table. Which Data Quality dimension is most directly violated?
Answer: D
Explanation:
The primary issue is Data Integrity, specifically referential integrity. The Orders record references a Customer_ID that does not correspond to an existing Customer record, meaning the relationship defined by the data model has been violated.
Integrity concerns whether structural relationships and constraints among data elements remain valid. In relational environments, this commonly includes primary-key uniqueness, foreign-key relationships, mandatory relationships, and cardinality constraints.
A customer identifier could be syntactically valid and populated, yet still fail integrity because no corresponding parent entity exists. This illustrates why integrity is different from completeness or validity.
The DMBOK2 dimension model explicitly associates integrity with unique identifiers, cardinality, and referential integrity concepts.
Remediation should determine why the orphan record occurred. Potential causes include incorrect load sequencing, deletion of the parent record, integration failure, transformation defects, or absence of database constraints.
Metadata and Data Modeling establish the expected relationship; Data Quality controls then measure whether operational data conforms to it.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Integrity; Referential Integrity; Chapter 5 - Relationships and Keys; Data Integration Controls.
NEW QUESTION # 15
The implementation of a 'Master Data Repository', which is integrated across the enterprise, is an example of which integration approach?
Answer: E
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 # 16
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