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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Quality Improvement | 20% | - Validation Rules - Data Cleansing Techniques - Standardization - Data Enrichment |
| Topic 2: Data Quality Assessment and Measurement | 25% | - Root Cause Analysis - Benchmarking - Metrics and KPIs - Profiling Techniques |
| Topic 3: Data Quality Fundamentals | 20% | - Definition of Data Quality - Dimensions of Data Quality (completeness, accuracy, consistency, timeliness, validity) - Data Quality in the Data Lifecycle |
| Topic 4: Data Quality Frameworks and Standards | 20% | - DAMA DMBOK Framework - ISO 8000 (Data Quality) - Other Industry Standards |
| Topic 5: Data Quality Governance | 15% | - Continuous Monitoring - Policies and Procedures - Data Stewardship - Data Quality Roles and Responsibilities |
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NEW QUESTION # 100
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 # 101
A Data Quality rule has been implemented and the defect rate has fallen below the agreed threshold. What activity should continue?
Answer: C
Explanation:
The organization should continue monitoring the quality metric. A successful remediation does not guarantee that the underlying process will remain controlled indefinitely.
Systems change, source applications are upgraded, personnel and suppliers change, reference values evolve, interfaces are modified, and business rules are revised. Any of these events can cause previously corrected defects to reappear.
A mature Data Quality lifecycle therefore treats improvement as continuous rather than as a one-time cleansing exercise. DAMA's revision of Chapter 13 specifically clarifies the Data Quality Improvement Lifecycle and links it to established continuous-improvement cycles.
Monitoring should confirm that the result remains within agreed thresholds and should provide trend information so deterioration can be detected before it creates significant business impact.
The frequency of monitoring may be reduced after a process demonstrates sustained stability, but the decision should be risk-based. Critical Data Elements generally justify stronger ongoing oversight.
The rule, threshold, owner, and measurement method should remain documented as metadata.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Monitoring; Control; Continuous Improvement; Metrics; Thresholds; Data Quality Scorecards.
NEW QUESTION # 102
A search engine database is populated by a web crawler or spider software that usually processes as:
Answer: D
Explanation:
Web-scale search-engine repositories commonly operate according to BASE: Basically Available, Soft State, Eventually Consistent. BASE is associated with distributed and many NoSQL data architectures where availability, horizontal scalability, and tolerance of distributed processing are prioritized over the immediate transactional consistency expected from traditional ACID systems.
A web crawler collects and indexes content continuously across very large distributed environments. Different nodes or indexes may temporarily contain slightly different versions of information while updates propagate.
The design is acceptable because the system is expected to converge toward consistency rather than guarantee that every replica reflects the identical state at every instant. DAMA-aligned material explicitly contrasts BASE with traditional ACID approaches for large-scale distributed data processing.
ACID-Atomicity, Consistency, Isolation, Durability-is generally associated with transaction-oriented relational systems where individual transactions require strict integrity guarantees. Search indexing has different workload characteristics and can tolerate temporary inconsistency.
The Data Quality implication is that "consistency" must be evaluated according to architectural context.
Temporary divergence can be expected behavior in an eventually consistent platform rather than evidence of a defect, provided convergence occurs within acceptable business thresholds.
Reference Topics: DAMA-DMBOK2 - Data Storage and Operations; Distributed Databases; NoSQL; ACID versus BASE; Availability; Consistency.
NEW QUESTION # 103
Which of the following is a reason why organisations do not dispose of non-value-adding information?
Answer: C
Explanation:
The correct answer is storage is cheap and easily expanded. DAMA-DMBOK2 discusses retention and disposal as important lifecycle-management responsibilities. Although information that no longer provides business, legal, regulatory, historical, or evidentiary value should normally be disposed of according to approved retention policies, organizations often postpone disposal because modern storage appears inexpensive and technically easy to expand.
This reasoning is deceptive. The acquisition cost of storage is only one component of total information- management cost. Retaining unnecessary information also increases backup requirements, recovery time, discovery obligations, privacy exposure, security risk, metadata-management effort, migration complexity, and the volume of information that must be governed. DMBOK2 therefore stresses that non-value-adding information should not be retained merely because storage capacity is readily available.
From a Data Quality perspective, excessive retention also increases the population of obsolete, redundant, and potentially inconsistent information. This makes profiling, lineage analysis, master-data reconciliation, and authoritative-source identification more difficult.
A sound governance program consequently combines retention schedules, legal requirements, metadata classification, defensible disposal procedures, and clear accountability so that data is retained for legitimate reasons rather than technological convenience.
Reference Topics: DAMA-DMBOK2 - Document and Content Management; Retention and Disposal; Information Lifecycle; Data Governance; Chapter 13 - Data Quality and Obsolete Data.
NEW QUESTION # 104
The DAMA Wheel contains:
Answer: A
Explanation:
The DAMA Wheel represents the principal Data Management Knowledge Areas defined by DAMA- DMBOK2. Data Governance occupies the central position because governance provides authority, coordination, standards, and oversight across the other disciplines. The surrounding areas include Data Architecture, Data Modeling and Design, Data Storage and Operations, Data Security, Data Integration and Interoperability, Document and Content Management, Reference and Master Data, Data Warehousing and Business Intelligence, Metadata Management, and Data Quality. DAMA descriptions of the framework identify these as Knowledge Areas rather than project deliverables or maturity dimensions.
The Wheel is deliberately integrative. No Data Management discipline operates effectively in complete isolation. For example, Data Quality relies on Governance for authority and accountability, Metadata Management for definitions and lineage, Reference and Master Data for consistent shared entities and codes, Data Architecture for structural context, and Integration for controlled movement between systems.
This interaction is central to Chapter 13. Data Quality cannot be reduced to cleansing defective records after problems appear. Sustainable quality requires coordinated management throughout the lifecycle and across the other DAMA knowledge areas.
Data management processes, deliverables, strategic initiatives, and maturity assessments are all legitimate DAMA concepts, but they are not what the DAMA Wheel itself principally represents.
Reference Topics: DAMA-DMBOK2 Chapter 1 - DAMA-DMBOK Framework; DAMA Wheel; Knowledge Areas; Chapter 3 - Governance; Chapter 13 - Data Quality Relationships with Other Knowledge Areas.
NEW QUESTION # 105
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