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| Section | Weight | Objectives |
|---|---|---|
| Data Quality Fundamentals | 20% | - Data Quality in the Data Lifecycle - Dimensions of Data Quality (completeness, accuracy, consistency, timeliness, validity) - Definition of Data Quality |
| Data Quality Improvement | 20% | - Data Cleansing Techniques - Data Enrichment - Validation Rules - Standardization |
| Data Quality Frameworks and Standards | 20% | - DAMA DMBOK Framework - Other Industry Standards - ISO 8000 (Data Quality) |
| Data Quality Governance | 15% | - Continuous Monitoring - Policies and Procedures - Data Stewardship - Data Quality Roles and Responsibilities |
| Data Quality Assessment and Measurement | 25% | - Root Cause Analysis - Profiling Techniques - Benchmarking - Metrics and KPIs |
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NEW QUESTION # 102
A relationship that allows an address to be used by multiple people, and each person can have multiple addresses, can be resolved:
Answer: E
Explanation:
The scenario describes a many-to-many relationship: one Person may use several Addresses, while one Address may be associated with several Persons. In relational modelling, this should be resolved using an associative entity-here, Person Address Usage-between Person and Address. The original many-to-many relationship is thereby replaced with two one-to-many relationships. DAMA-oriented references identify this approach directly under the addition of associative entities.
The associative entity normally contains foreign keys referencing both parent entities and may also contain attributes describing the association itself, such as address type, usage purpose, effective date, end date, or primary-address indicator.
Simply modifying the primary keys of Person or Address does not resolve the semantic relationship.
Changing foreign-key role names also changes terminology rather than cardinality. The term "partnership entity" is not the appropriate modelling construct.
This design has important Data Quality consequences. It permits explicit integrity constraints between the association and both parent entities, prevents ambiguous repeated columns, and allows the organization to validate rules such as valid effective periods and permitted address-use types.
Reference Topics: DAMA-DMBOK2 Chapter 5 - Relationships; Cardinality; Associative Entities; Resolving Many-to-Many Relationships; Chapter 13 - Referential Integrity and Consistency.
NEW QUESTION # 103
The need to manage data movement efficiently is a primary driver for:
Answer: E
Explanation:
The need to manage data movement efficiently is a primary business driver for Data Integration and Interoperability (DII). DAMA-DMBOK2 states this directly. Modern organizations operate hundreds or thousands of databases, applications, files, services, data stores, external interfaces, and analytical platforms.
Data must continually move among these environments, often across organizational boundaries.
Without disciplined integration management, data movement becomes fragmented, expensive, difficult to monitor, and highly dependent on duplicated point-to-point interfaces. DII addresses this problem by establishing controlled mechanisms for extraction, transformation, messaging, replication, orchestration, APIs, data virtualization, and other forms of information exchange.
From a Data Quality perspective, every movement of data presents an opportunity either to preserve quality or to damage it. Source-to-target mappings must maintain semantic meaning, transformations must be controlled, reference values must remain consistent, and lineage must show how values were altered.
Metadata Management therefore records mappings, interface definitions, transformation rules, and lineage.
Master Data Management uses integration mechanisms to distribute governed master and reference data consistently between systems.
Data Warehousing is a major consumer of integration capabilities, but the broader discipline whose explicit driver is efficient movement across systems is Data Integration and Interoperability.
Reference Topics: DAMA-DMBOK2 Chapter 8 - Business Drivers; Data Integration and Interoperability; Data Movement; Chapter 13 - Consistency, Integrity and Transformation Quality.
NEW QUESTION # 104
Business continuity is an aspect of Governance. What should a business continuity plan include?
Answer: D
Explanation:
A Business Continuity Plan is fundamentally concerned with maintaining essential business operations when normal services are disrupted. Therefore, the correct response is that it outlines how the business will continue operating during an unplanned disruption.
Within the DAMA-DMBOK2 framework, continuity concerns interact particularly with Data Governance, Data Storage and Operations, Data Security, and operational metadata. The DMBOK2 index explicitly identifies Business Continuity Plans and associates them with operational resilience and recovery concepts. A continuity plan must address how critical processes, information assets, personnel, technology, dependencies, and recovery arrangements will sustain or restore required business capability. The established definition of a BCP likewise centers on continued operation during unplanned service disruption.
Option E is incomplete: identifying potential disruptions belongs to risk assessment and continuity planning, but merely listing disruptions does not explain how operations will continue. Options B and D concern communications rather than continuity itself.
From a Data Quality perspective, continuity controls also protect availability, timeliness, integrity, and reliability. Recovery arrangements must ensure that restored data is complete, current enough for business use, and reconciled against authoritative sources after disruption.
Reference Topics: DAMA-DMBOK2 - Data Governance; Data Storage and Operations; Business Continuity; Disaster Recovery; Backup and Recovery; Chapter 13 - Timeliness, Integrity and Fitness for Purpose.
NEW QUESTION # 105
Examples of transformation include:
Answer: E
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 # 106
A financial transaction is captured correctly at 9:00 AM but does not become available to the fraud- monitoring system until 6:00 PM, although the business requirement is availability within five minutes.
Which Data Quality dimension is primarily violated?
Answer: C
Explanation:
The primary failure is Timeliness. The transaction may be completely accurate and complete, but it is not available within the period required by the consuming business process.
Timeliness evaluates whether data is available when needed for its intended use. The relevant threshold must therefore come from the business requirement rather than from an arbitrary technical target. In this scenario, the fraud-monitoring process requires the transaction within five minutes, while delivery occurs approximately nine hours later.
The root cause could exist in extraction frequency, integration queues, batch processing, network delays, source-system availability, or downstream ingestion. Lineage and operational metadata should be used to identify where the latency occurs.
Timeliness must also be distinguished from Currency. Currency asks whether information reflects a sufficiently recent real-world state; Timeliness asks whether data is delivered or available within the required period. A current transaction that arrives too late can therefore fail Timeliness even though the underlying value accurately represented reality when captured.
DAMA's revised DMBOK2 Chapter 13 recognizes both Timeliness and Currency as separate standard dimensions.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Timeliness; Currency; Data Quality Requirements; Data Integration; Operational Monitoring.
NEW QUESTION # 107
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