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| Section | Weight | Objectives |
|---|---|---|
| Data Quality Frameworks and Standards | 20% | - ISO 8000 (Data Quality) - DAMA DMBOK Framework - Other Industry Standards |
| Data Quality Governance | 15% | - Policies and Procedures - Continuous Monitoring - Data Stewardship - Data Quality Roles and Responsibilities |
| Data Quality Improvement | 20% | - Data Cleansing Techniques - Validation Rules - Standardization - Data Enrichment |
| 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 Assessment and Measurement | 25% | - Benchmarking - Profiling Techniques - Root Cause Analysis - Metrics and KPIs |
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NEW QUESTION # 84
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: C
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 # 85
A dashboard reports that Customer_Email Completeness is 98.5%, while the approved threshold is 99.5%.
What should the 98.5% figure be interpreted as?
Answer: D
Explanation:
The 98.5% value is a measured Data Quality result, and because it is below the approved 99.5% threshold, it represents a threshold breach.
A Data Quality metric quantifies observed performance. A threshold establishes the level of performance the business has agreed is acceptable. These should not be confused. The metric answers how are we performing?; the threshold answers is that performance acceptable?
The 98.5% completeness result says nothing about whether the populated email addresses are accurate. A field can achieve high completeness while containing incorrect or invalid values. Other rules may therefore be required for email syntax, uniqueness, deliverability, or factual correctness.
A threshold breach should trigger a predefined response appropriate to the criticality of the data. This may involve investigation, escalation, remediation, trend review, or root-cause analysis.
Metrics, calculation logic, measurement population, thresholds, ownership, and escalation rules should be documented as metadata so dashboard results remain interpretable and reproducible.
DAMA's framework explicitly places metrics, error detection, cleansing, and quality management within Data Quality Management.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Quality Metrics; Thresholds; Scorecards; Completeness; Monitoring.
NEW QUESTION # 86
Database monitoring tools measure key database metrics, such as:
Answer: C
Explanation:
DAMA-DMBOK2 identifies capacity, availability, cache performance, and user statistics as representative metrics captured by database-monitoring tools. Database monitoring is an operational control mechanism used by Database Administrators and platform teams to understand whether database infrastructure is available, adequately sized, responsive, and being used as expected. DMBOK2 explicitly describes monitoring tools as automating the observation of metrics such as capacity, availability, cache performance, and user statistics.
Capacity metrics indicate resource consumption and future growth requirements. Availability measures whether data services remain accessible when required. Cache-performance measures help identify inefficient access patterns and bottlenecks, while user statistics provide information about workload and database consumption.
The other choices mix design concepts, CRUD operations, or Data Quality concepts with operational monitoring measures. For example, normalization is a modelling technique rather than a routine runtime performance metric. Similarly, "create, read, update, delete" describes basic data operations rather than monitoring indicators.
Although database performance and Data Quality are distinct disciplines, poor operational performance can affect Data Quality dimensions such as timeliness and availability. Monitoring therefore supports the technical environment in which governed, reliable information is delivered to users and applications.
Reference Topics: DAMA-DMBOK2 - Data Storage and Operations; Database Operations; Capacity and Availability Management; Monitoring; Chapter 13 - Timeliness and Operational Fitness for Purpose.
NEW QUESTION # 87
Three common interaction models for data integration are:
Answer: B
Explanation:
DAMA-DMBOK2 identifies Point-to-Point, Hub-and-Spoke, and Publish-Subscribe as three common interaction models used to transfer data between systems.
In Point-to-Point, one system communicates directly with another. It is straightforward for a small number of systems but becomes difficult to maintain as interfaces proliferate. In Hub-and-Spoke, systems exchange information through a central hub, reducing direct dependencies and supporting greater consistency. In Publish-Subscribe, producing systems publish data while interested consumers subscribe to the relevant data service or event stream. DMBOK2 presents these three patterns explicitly within Data Integration and Interoperability.
The choice of pattern affects Data Quality as well as architecture. Point-to-point designs can create inconsistent transformations when several applications independently consume the same source. Hub-and- spoke designs can centralize transformations and reference mappings. Publish-subscribe supports scalable distribution but requires controlled message definitions and metadata so that every subscriber interprets data consistently.
DAMA therefore recommends choosing an interaction model according to functional, performance, latency, support, and governance requirements rather than allowing integration patterns to emerge independently.
Reference Topics: DAMA-DMBOK2 Chapter 8 - Data Integration and Interoperability; Interaction Models; Point-to-Point; Hub-and-Spoke; Publish-Subscribe; Canonical Models.
NEW QUESTION # 88
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: D
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 # 89
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