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| Section | Objectives |
|---|---|
| Topic 1: Data Quality Fundamentals | - Definition of Data Quality - Data Quality Dimensions (Accuracy, Completeness, Consistency, Timeliness, Validity, Uniqueness) |
| Topic 2: Data Quality Improvement | - Data Cleansing Methods - Root Cause Analysis |
| Topic 3: Data Quality Management | - Data Quality Governance - Roles and Responsibilities (Data Stewardship) |
| Topic 4: Data Quality Assessment | - Data Profiling Techniques - Data Quality Metrics and KPIs |
| Topic 5: Data Quality Operations | - Monitoring and Reporting - Data Quality Tools and Technologies |
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NEW QUESTION # 45
Which of the following is the best example of a 'documented data quality rule'?
Answer: E
Explanation:
Option D is the strongest example because it expresses a Data Quality requirement that is specific, measurable, associated with identified data, and explicitly connected to business need. It identifies the relevant data-transaction data from satellite systems-sets a measurable timeliness threshold of 12:05 a.m., and explains the business reason: ensuring that branches have access to actual customer balances for the required operating window. Published versions of this DAMA question identify the same choice.
This is fundamentally a Timeliness rule. DMBOK2 emphasizes that Data Quality requirements should be defined according to business expectations rather than vague aspirations. A useful rule needs sufficient precision to support objective measurement, exception reporting, and remediation.
Option E captures an important quality expectation-actual customer balances-but it lacks the same precise operational threshold. Option A is a security/confidentiality requirement rather than a Data Quality rule.
Options B and C primarily describe processing procedures and do not express the business-quality requirement as cleanly.
Once documented, the rule should be managed as metadata, assigned to an accountable steward, linked to its Critical Data Elements, monitored through metrics, and escalated when the specified threshold is breached.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Quality Requirements; Data Quality Rules; Timeliness; Measurement; Business Impact; Metadata Management.
NEW QUESTION # 46
A dataset contains every required customer record, but 8% of the telephone numbers belong to different people. Which statement is correct?
Answer: B
Explanation:
The dataset can be complete while remaining inaccurate. Completeness measures whether the required records and values are present; Accuracy measures whether those values correctly represent reality.
If every customer has a telephone number, the field may achieve 100% completeness. However, if 8% of those numbers belong to other people, the values are inaccurate.
This distinction is one of the most important principles in Data Quality measurement. A populated field is not automatically trustworthy. Similarly, a syntactically valid value is not necessarily accurate.
DAMA-aligned guidance explicitly distinguishes these dimensions and notes that a complete dataset may still contain incorrect values.
The appropriate quality scorecard should therefore present completeness and accuracy separately rather than treating one as evidence of the other.
For high-risk customer contact processes, the business may define independent thresholds for both dimensions. Verification services, customer confirmation, authoritative sources, and exception handling may be needed to improve accuracy after basic completeness has already been achieved.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Completeness; Accuracy; Data Quality Dimensions; Metrics; Fitness for Purpose.
NEW QUESTION # 47
Reference data is often a list of code values with their full names. One example is:
Answer: B
Explanation:
Country codes associated with country names are a classic example of Reference Data. DAMA-DMBOK2 defines Reference Data as data used to characterize or classify other data or to relate organizational data to externally defined information. The simplest reference-data structure consists of a code and its corresponding description. DMBOK2 specifically uses geographic and standards-based examples, including country codes such as DE, US, and TR.
A code such as GB therefore acts as a standardized machine-processable value, while "United Kingdom" supplies the human-readable meaning. Such code lists are reused across applications, integrations, reporting platforms, Master Data systems, and analytical environments.
Reference Data quality is important because inconsistent code sets can create widespread downstream defects.
If different systems use incompatible country codes, the organization may experience failed integrations, incorrect aggregation, duplicate mappings, reporting discrepancies, and inconsistent interpretation. Data Governance should therefore establish authoritative sources and stewardship responsibilities for important reference domains.
Metadata Management records the meaning, provenance, permitted values, relationships, and mappings between code sets. Master Data Management frequently consumes these governed codes to classify master entities such as customers, suppliers, products, and locations.
The other choices describe relationships or transformations, but they do not represent the straightforward code- value/description list structure identified by DAMA.
Reference Topics: DAMA-DMBOK2 Chapter 10 - Reference and Master Data; Reference Lists; Code Sets; Authoritative Sources; Chapter 13 - Validity, Consistency and Integrity.
NEW QUESTION # 48
Profiling reveals that an Age field has a minimum value of -7 and a maximum value of 263. What should the Data Quality team do first?
Answer: D
Explanation:
The values should first be treated as potential exceptions requiring investigation. Profiling identifies anomalies; it does not automatically prove that every unusual value is incorrect or specify the appropriate remediation.
Negative age and 263 years are highly improbable for a conventional person-age attribute, suggesting a validity or reasonableness defect. However, the Data Quality team should confirm the business definition, unit of measure, derivation logic, source mappings, special-value conventions, and intended population before changing the records.
Blindly replacing values with an average introduces fabricated data and destroys traceability. Deleting the records may also remove legitimate information elsewhere in the record. Increasing the datatype range addresses technical storage rather than semantic correctness.
A disciplined Data Quality process moves from profiling to rule validation, issue assessment, root-cause analysis, remediation, and monitoring. The business rule may ultimately specify an acceptable age range, but that rule should be governed and documented rather than inferred from individual anomalies.
Metadata is essential because it should describe whether Age is stored directly, calculated from Date of Birth, or encoded using another convention.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Profiling; Validity; Reasonableness; Root-Cause Analysis; Data Quality Rules; Metadata.
NEW QUESTION # 49
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?
Answer: D
Explanation:
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.
NEW QUESTION # 50
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