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| Section | Objectives |
|---|---|
| Data Quality Assessment | - Data Quality Metrics and KPIs - Data Profiling Techniques |
| Data Quality Operations | - Monitoring and Reporting - Data Quality Tools and Technologies |
| Data Quality Management | - Roles and Responsibilities (Data Stewardship) - Data Quality Governance |
| Data Quality Fundamentals | - Definition of Data Quality - Data Quality Dimensions (Accuracy, Completeness, Consistency, Timeliness, Validity, Uniqueness) |
| Data Quality Improvement | - Root Cause Analysis - Data Cleansing Methods |
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NEW QUESTION # 58
How is Data Governance defined?
Answer: E
Explanation:
DAMA-DMBOK2 defines Data Governance as the exercise of authority and control over the management of data assets. This definition distinguishes governance from the operational execution of Data Management.
Independent literature describing DAMA's framework uses the same core formulation, and the DAMA Wheel places Data Governance across the other knowledge areas through this authority-and-control function.
Governance establishes decision rights, accountability, policies, standards, stewardship, issue escalation, compliance expectations, and mechanisms for monitoring whether data is being managed appropriately. It determines who is authorized to make decisions and what rules must be followed; individual Data Management knowledge areas then execute the corresponding operational activities.
Option E is closer to the broad definition of Data Management, because it refers to planning, implementation, and control across the information lifecycle. Option A describes an assessment or maturity activity. Option B describes selected governance benefits rather than its definition, while option C characterizes a collection of functions.
For Data Quality, governance provides the authority needed to define critical data, approve quality requirements and thresholds, assign accountability, prioritize remediation, and enforce standards. Without this authority structure, profiling may identify defects but the organization may lack the decision mechanisms required to correct their underlying causes.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Data Governance Definition; Authority and Control; Policies and Standards; Chapter 13 - Data Quality Governance.
NEW QUESTION # 59
In data security, which of the following is not one of the four A's:
Answer: B
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 # 60
The goals of data storage and operations are:
Answer: C
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 # 61
Mapping requirements and rules for moving data from source to target enables:
Answer: D
Explanation:
Source-to-target mapping enables Transformation. DAMA-DMBOK2 treats mapping as closely synonymous with transformation because a mapping defines how data in one source structure will be converted into the structure, format, representation, or value required by the target.
A mapping specification typically identifies the source attribute, target attribute, extraction conditions, target population rules, intermediate staging transformations, calculations, lookup requirements, and any changes required to make the source data conform to the target representation. DMBOK2 specifically explains that mapping sources to targets involves defining the rules for transforming information from one location and format into another.
Extraction simply retrieves data from the source. Loading places data into the target. Transformation is the activity that applies structural, syntactic, semantic, or value-level modifications between those stages.
The Data Quality connection is substantial. Mappings may standardize dates, convert units, harmonize codes, resolve reference values, remove duplicates, or enforce business rules. If mapping metadata is incomplete or incorrect, the transformation process can introduce rather than correct quality defects.
For this reason, source-to-target mapping should be governed, version-controlled, documented as metadata, traceable through lineage, and validated against agreed business definitions and Data Quality requirements.
Reference Topics: DAMA-DMBOK2 Chapter 8 - Map Data Sources to Targets; Transformation; ETL
/ELT; Metadata Lineage; Chapter 13 - Data Cleansing and Standardization.
NEW QUESTION # 62
Periodic archiving of transaction data from a production CRM system is critical for:
Answer: B
Explanation:
Periodic archiving is critical for maintaining database performance. As a production CRM accumulates historical transactions, active tables and indexes can become increasingly large. This increases storage consumption, backup duration, index-maintenance overhead, and the quantity of data that database engines must process during operational queries.
DAMA-DMBOK2 treats archiving as an important Data Storage and Operations activity. Historical information that remains subject to retention requirements but is no longer frequently needed for operational processing can be moved to suitable archival storage. DAMA-aligned guidance for this scenario specifically links periodic transaction archiving with maintaining production database performance.
Archiving is not the same as arbitrary deletion. Retention policies, legal obligations, recovery requirements, auditability, and business value determine how long data must remain accessible and where it should be stored. The archive must also be recoverable and appropriately secured.
Data Quality implications include maintaining integrity and traceability during migration to the archive.
Records should remain complete, relationships should be preserved, and metadata should indicate retention status and archival location.
Providing reporting sources or managing deleted customers may be secondary considerations, but neither is the principal purpose described in the question.
Reference Topics: DAMA-DMBOK2 Chapter 6 - Data Storage and Operations; Archiving; Database Performance; Retention; Chapter 13 - Integrity and Historical Data.
NEW QUESTION # 63
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