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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Quality Frameworks and Standards | 20% | - ISO 8000 (Data Quality) - DAMA DMBOK Framework - Other Industry Standards |
| Topic 2: Data Quality Fundamentals | 20% | - Definition of Data Quality - Dimensions of Data Quality (completeness, accuracy, consistency, timeliness, validity) - Data Quality in the Data Lifecycle |
| Topic 3: Data Quality Assessment and Measurement | 25% | - Root Cause Analysis - Benchmarking - Metrics and KPIs - Profiling Techniques |
| Topic 4: Data Quality Improvement | 20% | - Standardization - Validation Rules - Data Enrichment - Data Cleansing Techniques |
| Topic 5: Data Quality Governance | 15% | - Data Stewardship - Policies and Procedures - Continuous Monitoring - Data Quality Roles and Responsibilities |
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NEW QUESTION # 115
A customer is shown as "Active" in the CRM but "Closed" in the billing system at the same point in time.
Both systems use the same customer identifier. Which Data Quality dimension is primarily affected?
Answer: B
Explanation:
The primary dimension affected is Consistency. Consistency evaluates whether equivalent information agrees across datasets, systems, representations, or related attributes when business rules require agreement.
The same customer identifier is represented with conflicting lifecycle statuses at the same time. Each value might individually satisfy its local datatype and permitted-value rules, so both could be valid in isolation.
However, unless a legitimate business rule explains the difference, the two representations are inconsistent.
Resolving the issue requires more than choosing one value arbitrarily. The organization must establish the authoritative source for customer status, understand whether the systems apply different definitions of
"Active" and "Closed," examine synchronization timing, and trace transformation or integration logic.
Metadata should document the business meaning and source authority, while lineage should reveal how status information is propagated.
Master Data Management may also be relevant where customer-state information is shared across multiple operational systems. Governance must determine ownership and conflict-resolution rules.
DAMA's revised Chapter 13 identifies Consistency among the standardized Data Quality dimensions and explicitly strengthens its relationship with Metadata, Reference/Master Data, and Data Integration.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Consistency; Authoritative Sources; Metadata Management; Master Data Management; Data Integration.
NEW QUESTION # 116
Which DMBoK knowledge area is most likely responsible for a high percentage of returned mail?
Answer: D
Explanation:
A high percentage of returned mail is primarily a Data Quality problem. Returned correspondence frequently results from inaccurate, incomplete, obsolete, incorrectly formatted, or otherwise unusable address data. The operational symptom therefore indicates that address information is failing to meet its intended business purpose.
Published versions of this DAMA question identify Data Quality as the intended knowledge area. The underlying principle aligns directly with DMBOK2 Chapter 13: quality is assessed relative to the requirements of the business process consuming the data.
Several quality dimensions may contribute. Accuracy concerns whether the address correctly represents the customer's actual location. Completeness concerns missing apartment numbers, postal codes, or address components. Validity evaluates whether values conform to accepted formats and domains. Currency addresses whether a previously correct address has become obsolete. Consistency can also be involved where address information differs between systems.
Reference and Master Data Management may provide customer-master consolidation and standardized geographic reference values, but the direct defect represented by returned mail is poor-quality address data.
A mature response should involve profiling returned-mail records, determining defect patterns and root causes, establishing address-quality rules, correcting affected records, and introducing preventive validation at capture.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Fitness for Purpose; Accuracy; Completeness; Validity; Currency; Profiling; Root-Cause Analysis.
NEW QUESTION # 117
The goal of data governance is to enable an organisation to manage data as an asset. To achieve this, the DG programs must be:
Answer: D
Explanation:
DAMA-DMBOK2 explicitly states that a Data Governance program must be sustainable. Governance is not a temporary implementation project that ends after policies, committees, or stewardship roles are established. It is an ongoing organizational capability requiring continuing leadership, sponsorship, ownership, decision rights, and operational integration.
The DMBOK2 governance guidance describes sustainable governance as "sticky": it must survive beyond its initial implementation and become embedded in normal business and Data Management practices.
Sustainable governance depends specifically on business leadership, sponsorship, and ownership.
This distinction matters for Data Quality because quality improvement is likewise continuous. New applications, data sources, business processes, regulatory requirements, and transformations continually create new risks. Governance must therefore continue assigning accountability, approving definitions and quality rules, resolving cross-domain disputes, and overseeing remediation.
Options focused on financial registration or assigning a dollar value confuse data-as-an-asset thinking with formal accounting treatment. Data valuation can support investment decisions, but it is not a prerequisite for Data Governance. Likewise, treating governance as a fixed-duration initiative contradicts the operating model described by DAMA.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Data Governance Goals and Principles; Sustainable Governance; Leadership; Sponsorship; Ownership; Chapter 13 - Data Quality Governance.
NEW QUESTION # 118
Which of the following is the best example of a 'documented data quality rule'?
Answer: D
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 # 119
Reference data is often a list of code values with their full names. One example is:
Answer: A
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 # 120
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