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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Quality Frameworks and Standards | 20% | - Other Industry Standards - DAMA DMBOK Framework - ISO 8000 (Data Quality) |
| Topic 2: Data Quality Assessment and Measurement | 25% | - Root Cause Analysis - Profiling Techniques - Metrics and KPIs - Benchmarking |
| Topic 3: Data Quality Fundamentals | 20% | - Dimensions of Data Quality (completeness, accuracy, consistency, timeliness, validity) - Definition of Data Quality - Data Quality in the Data Lifecycle |
| Topic 4: Data Quality Governance | 15% | - Policies and Procedures - Data Stewardship - Continuous Monitoring - Data Quality Roles and Responsibilities |
| Topic 5: Data Quality Improvement | 20% | - Data Enrichment - Standardization - Data Cleansing Techniques - Validation Rules |
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NEW QUESTION # 84
In data security, which of the following is not one of the four A's:
Answer: E
Explanation:
Agile is not one of the four A's of Data Security. DAMA security guidance groups core security processes around Access, Audit, Authentication, and Authorization. These capabilities collectively control who can interact with information resources, establish identity, determine permitted actions, and provide evidence of activity for monitoring and accountability. DAMA-aligned security references identify these four concepts directly.
Authentication confirms the identity of a user or system. Authorization determines what an authenticated identity is permitted to do. Access concerns the actual ability to obtain or interact with data and services.
Audit provides traceability by recording and reviewing relevant activities.
Agile, by contrast, is an approach to iterative delivery and project or product development. It may influence how security controls are implemented within development lifecycles, but it is not itself one of the foundational security-control categories identified in this model.
Data Security and Data Quality are separate but related disciplines. Unauthorized modifications can compromise accuracy and integrity, while weak auditability can prevent organizations from identifying how data was altered. Appropriate security controls therefore help preserve the reliability of governed information.
Metadata classification also supports these controls by identifying sensitive data and linking it to access and protection requirements.
Reference Topics: DAMA-DMBOK2 Chapter 7 - Data Security; Access; Authentication; Authorization; Audit; Data Governance; Chapter 13 - Integrity and Controlled Data Modification.
NEW QUESTION # 85
What is the final step in the development of a business-data-driven roadmap?
Answer: B
Explanation:
A business-data-driven roadmap translates the desired enterprise data architecture into an executable sequence of initiatives. DAMA-DMBOK2 explains that business capabilities consume and produce data, which creates dependencies between capabilities. The roadmap begins with relatively independent capabilities and progresses toward capabilities whose execution depends on data generated elsewhere. The architecture therefore establishes a coherent dependency chain before implementation work is organized.
The decisive final planning action is to turn that dependency sequence into implementable projects.
Consequently, establishing the sequence of projects that will establish the data value chain is the best answer.
Describing the data value chain, analysing data flows, identifying dependency challenges, and resequencing capabilities are analytical steps that precede portfolio sequencing.
DMBOK2 states that the roadmap should move from capabilities with the least dependency toward those with progressively greater dependencies, thereby providing the implementation order. It also emphasizes integrating the Enterprise Data Architecture roadmap with enterprise milestones, resources, and business- capability workstreams.
From a Data Quality perspective, sequencing matters because upstream master and reference data must reach an acceptable quality state before downstream transactional processes can depend on it.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Data Architecture; Develop a Roadmap; Data Dependencies; Data Value Chain; Chapter 13 - Data Quality and other Knowledge Areas.
NEW QUESTION # 86
The role of Metadata in Data Management is:
Answer: A
Explanation:
The broad role of Metadata is to help organizations understand their data, systems, and workflows. Metadata provides the context required to interpret data correctly and connect business meaning with technical implementation and operational processing. This answer is also explicitly identified in DAMA-oriented certification material.
Business metadata explains concepts, definitions, rules, ownership, stewardship, and terminology. Technical metadata describes tables, columns, datatypes, interfaces, transformations, and system structures. Operational metadata captures information about processing, execution, schedules, and usage. Together, these categories explain what information means, where it resides, how it moves, and how it is used.
Metadata can certainly improve decision-making, support Big Data solutions, group concepts, and assist reporting, but those are secondary outcomes. Option C expresses its enterprise-wide role most comprehensively.
Its connection to Data Quality is fundamental. Quality cannot be assessed consistently without metadata explaining the intended meaning and acceptable characteristics of a data element. Lineage allows a defect to be traced to its origin, while business definitions establish the context for accuracy and validity measurements.
Reference Topics: DAMA-DMBOK2 Metadata Management - Business, Technical and Operational Metadata; Data Lineage; Business Glossary; Chapter 13 - Data Quality Rules and Metadata Dependencies.
NEW QUESTION # 87
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 # 88
Critical Data is most often used in:
Answer: C
Explanation:
All of these is correct because DAMA-DMBOK2 defines criticality according to the business use and impact of the data rather than by a particular technical characteristic. Critical Data Elements are commonly associated with regulatory, financial, or management reporting; operational business needs; measurement of product quality and customer satisfaction; and strategic initiatives such as competitive differentiation. DAMA's revised Chapter 13 also explicitly adds and clarifies the concept of Critical Data Elements within the Data Quality framework.
The purpose of identifying critical data is prioritization. Organizations cannot apply identical levels of profiling, monitoring, stewardship, remediation, and control to every data element. Data whose failure could create substantial operational, financial, legal, regulatory, customer, or reputational impact receives greater management attention.
For example, values feeding regulatory reports require stringent accuracy and traceability; operational data may require high availability and timeliness; customer-satisfaction measures require reliable and consistent inputs; and strategically important data can affect competitive decisions. Current DAMA-aligned material confirms these same usage categories for Critical Data Elements.
Once Critical Data Elements are identified, they should be linked to business definitions, Data Owners and Stewards, quality dimensions, measurable rules, lineage, authoritative sources, thresholds, and issue- management processes.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Critical Data Elements; Business Drivers; Data Quality Requirements; Prioritization; Data Governance; Metadata Management.
NEW QUESTION # 89
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