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| Section | Objectives |
|---|---|
| Data Quality Fundamentals | - Data Quality Dimensions (Accuracy, Completeness, Consistency, Timeliness, Validity, Uniqueness) - Definition of Data Quality |
| Data Quality Operations | - Monitoring and Reporting - Data Quality Tools and Technologies |
| Data Quality Assessment | - Data Quality Metrics and KPIs - Data Profiling Techniques |
| Data Quality Improvement | - Root Cause Analysis - Data Cleansing Methods |
| Data Quality Management | - Roles and Responsibilities (Data Stewardship) - Data Quality Governance |
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質問 # 19
Which of the following is a reason why organisations do not dispose of non-value-adding information?
正解：D
解説：
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.
質問 # 20
A company has five systems that can update a customer's legal name, and no system is designated as authoritative. What is the greatest Data Quality risk?
正解：B
解説：
The principal risk is inconsistent customer-name values across systems. When multiple applications can independently update the same business fact without an authoritative source or coordinated Master Data process, conflicting representations are likely to emerge.
The problem is architectural and governance-related as much as it is a Data Quality issue. The organization must determine where the authoritative value is created, who is responsible for approving changes, how updates propagate, and how conflicts are resolved.
DAMA's public framework emphasizes a single trusted or consistent approach to shared core entities through Reference and Master Data Management.
The relevant Data Quality dimensions include Consistency, Accuracy, Currency, and potentially Uniqueness.
An application may hold a technically valid legal name but still contain an outdated or conflicting value.
Metadata and lineage should document which systems create, master, distribute, or merely consume the attribute.
Reference Topics: DAMA-DMBOK2 - Authoritative Sources; Master Data Management; Data Architecture; Consistency; Lineage; Stewardship.
質問 # 21
A pensioner who usually receives a quarterly bill of around $300 was sent a $100,000,000 electricity bill.
They were a victim of poor data quality checks in which dimension?
正解：B
解説：
The failed control is principally Reasonableness. DAMA treats reasonableness as the extent to which data values or patterns conform to credible expectations. The current DMBOK2 revision recognizes Reasonableness among the standard Data Quality dimensions, alongside Accuracy, Validity, Completeness, Integrity, Uniqueness, Timeliness, Currency and Consistency.
A $100,000,000 residential electricity bill may be syntactically valid: the field may accept the number, the record may satisfy referential constraints, and the value may have arrived on time. Nevertheless, it is grossly inconsistent with the customer's historical pattern of approximately $300 per quarter. A reasonableness control would therefore compare the amount with expected ranges, historical consumption, statistical limits, peer-group distributions, or business thresholds and flag the value before billing.
This illustrates an important distinction from Accuracy. Accuracy asks whether the stored value correctly represents reality. Reasonableness asks whether the value is credible in context. The absurd magnitude itself is detectable without first obtaining an independently verified "true" bill value.
Practical controls include range checks, deviation-from-history rules, z-score/outlier detection, control limits, and exception thresholds. Such rules should be governed as metadata and owned by an appropriate Data Steward.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Quality Dimensions; Reasonableness; Measurement and Monitoring; Statistical Process Control; Data Quality Rules.
質問 # 22
A minimal super key is:
正解：F
解説：
A candidate key is a minimal super key. A super key is any set of attributes sufficient to uniquely identify an entity instance, but it may contain attributes that are unnecessary for uniqueness. A candidate key removes that redundancy: if any attribute is removed from the candidate key, the remaining attributes no longer uniquely identify the entity.
DAMA-DMBOK2 makes this distinction explicitly: a candidate key is a minimal set of one or more attributes identifying an entity instance, and "minimal" means that no subset of the candidate key can perform the same unique-identification function.
Option B is incomplete because a set can uniquely identify an entity while still containing unnecessary attributes; that would qualify as a super key but not necessarily a minimal super key. A surrogate key is different: it is an artificial identifier introduced primarily for technical identification. Foreign-key composition and physical index structures likewise do not define candidate-key minimality.
This concept has direct Data Quality implications. Correctly defined candidate and primary keys support uniqueness and integrity, prevent duplicate entity instances, enable reliable referential relationships, and improve entity matching within Master Data Management. Key definitions should also be captured as structural metadata so profiling and quality rules can consistently test duplicate and orphan conditions.
Reference Topics: DAMA-DMBOK2 Chapter 5 - Data Modeling and Design; Keys; Candidate Keys; Chapter 13 - Uniqueness and Integrity; Metadata Management; Master Data Management.
質問 # 23
A company requires every system to use the same ISO country-code list managed centrally. This is primarily an example of:
正解：D
解説：
This is Reference Data Management. Country codes represent a controlled set of values used to classify or contextualize other data. Managing them centrally helps ensure that all systems interpret the same codes consistently.
Without centralized governance, one system might use GB, another UK, and another proprietary numeric values. Those differences create mapping complexity, failed integrations, inaccurate aggregation, and inconsistent reporting.
Reference Data Management establishes authoritative sources, stewardship, allowed values, mappings, effective dates, distribution processes, and change control. DAMA's public framework describes Reference and Master Data Management as ensuring consistency in core shared information across organizational units.
The Data Quality dimensions most directly supported are Validity and Consistency. Validity ensures values belong to an approved domain; consistency ensures equivalent concepts are represented compatibly across systems.
Metadata should document the standard, meaning, owner, and version of each reference domain.
Reference Topics: DAMA-DMBOK2 Chapter 10 - Reference Data; Controlled Code Sets; Authoritative Sources; Chapter 13 - Validity and Consistency.
質問 # 24
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