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| Section | Weight | Objectives |
|---|---|---|
| Data Quality Concepts and Principles | 15% | - Definition and importance of data quality - Data quality in DMBOK framework - Data quality dimensions |
| Data Quality Assessment and Profiling | 25% | - Data profiling techniques - Assessment methods and tools - Defining quality rules and metrics |
| Data Quality Improvement | 20% | - Root cause analysis - Data cleansing and standardization - Monitoring and continuous improvement |
| Data Quality Strategy and Governance | 20% | - Roles and responsibilities - Integration with data governance - Data quality strategy and policy |
| Tools, Techniques and Implementation | 20% | - Data quality in lifecycle management - Business value and ROI - Data quality tools and technologies |
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NEW QUESTION # 17
A Data Quality team wants to understand whether defect rates are improving month by month rather than viewing only the latest percentage. Which approach is most useful?
Answer: D
Explanation:
Trend analysis is most useful because it shows how a quality metric changes over time rather than presenting a single isolated measurement.
A current defect rate of 4% has different implications depending on whether the previous months were 12%,
8%, and 6%, or 1%, 2%, and 3%. The first pattern indicates improvement; the second indicates deterioration.
Trend analysis therefore provides context for management decisions, helps determine whether remediation is producing sustained results, and can identify emerging degradation before thresholds are breached.
For stable, regularly measured processes, statistical control charts can add further value by distinguishing routine variation from signals that warrant investigation.
Metrics should be measured consistently over time. Changing definitions, populations, or calculation methods without documenting them can make apparent trends misleading.
Metadata should preserve metric definitions, threshold changes, and measurement logic to ensure comparability.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Monitoring; Trend Analysis; Statistical Control; Metrics; Continuous Improvement.
NEW QUESTION # 18
Multiple data warehouses, lakes, and swamps are an indicator that the organisation values:
Answer: E
Explanation:
A proliferation of independently implemented data warehouses, lakes, and poorly governed "data swamps" commonly indicates that the organization has emphasized technology acquisition over establishing an integrated, authoritative view of enterprise data. The issue is not the existence of multiple technologies by itself; distributed platforms can be justified by legitimate architectural requirements. The warning sign is uncontrolled duplication without clear authoritative sources, enterprise architecture, governance, lineage, and consistency controls.
DAMA-DMBOK2 treats Data Architecture as the mechanism for aligning data assets with enterprise strategy and reducing unnecessary fragmentation. A mature architecture identifies authoritative sources, data flows, integration patterns, shared definitions, and the required relationship between operational and analytical stores. The DAMA framework also emphasizes that technologies should support business and data- management objectives rather than dictate them.
From a Data Quality perspective, uncontrolled proliferation creates competing versions of values, inconsistent transformations, duplicate master records, contradictory metrics, and unclear ownership. These conditions undermine consistency and make root-cause analysis difficult.
A "single point of truth" does not necessarily require one physical database. It requires controlled authority and consistent semantics across the landscape.
Reference Topics: DAMA-DMBOK2 - Data Architecture; Data as an Asset; Authoritative Sources; Data Integration; Metadata; Data Quality Consistency.
NEW QUESTION # 19
A security mechanism that searches for customer bank account details in outgoing emails is achieving the goal of:
Answer: A
Explanation:
The mechanism is designed to meet confidentiality and privacy requirements. Customer bank-account details constitute sensitive financial information. Inspecting outgoing email for such values is a form of Data Loss Prevention control intended to identify or prevent inappropriate disclosure before sensitive information leaves the organization's controlled environment.
DAMA-DMBOK2 treats confidentiality and privacy as fundamental Data Security requirements. Its electronic-communication guidance warns that restricted or confidential information should not be sent through insecure communication channels because messages can be intercepted, forwarded, or otherwise disclosed after leaving the sender's control. DAMA International's own privacy practices likewise classify bank-account and similar financial information as protected personal/financial data requiring security safeguards against unauthorized disclosure.
The other responses concern transparency, definitions, user experience, or availability and do not address the security objective demonstrated in the scenario.
Data Governance determines the classifications and policies governing such information; Metadata Management can record sensitivity classifications against physical data elements; and security technology then enforces the resulting controls.
Data Quality remains relevant because confidentiality controls must distinguish genuinely sensitive values accurately. Poor classification or inaccurate detection rules may either expose protected information or unnecessarily block legitimate communication.
Reference Topics: DAMA-DMBOK2 Chapter 7 - Confidentiality and Privacy; Electronic Communication Security; Sensitive Data; Data Loss Prevention; Data Governance.
NEW QUESTION # 20
A Data Quality score is calculated from Completeness, Accuracy, and Timeliness. Why might the dimensions be weighted differently?
Answer: B
Explanation:
Dimensions may be weighted differently because their business impact is not necessarily equal for a particular dataset or process.
For an emergency-response system, Timeliness may be more critical than completeness of optional descriptive fields. For regulatory reporting, Accuracy and Integrity may carry substantially greater risk than minor delays. For a marketing contact list, completeness and currency may dominate.
A composite Data Quality score should therefore reflect business priorities rather than assuming that each dimension contributes identical value. Weighting must be transparent and governed; otherwise a high score in low-impact dimensions could hide serious failure in a critical one.
The first step remains defining business requirements and Critical Data Elements. Once those are established, dimensions, thresholds, and weights can be linked to documented business consequences.
A score should also retain underlying component metrics. A single aggregate percentage is insufficient for diagnosis because two datasets with the same overall score may have very different risk profiles.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Metrics; Scorecards; Business Impact; Critical Data Elements; Quality Dimensions; Thresholds.
NEW QUESTION # 21
A form permits users to enter any text into a field that should contain an ISO country code. What is the strongest preventive Data Quality control?
Answer: C
Explanation:
The strongest preventive control is to restrict data entry to the approved country-code domain. Prevention at the point of creation is generally more effective and less costly than repeatedly correcting invalid values downstream.
A controlled drop-down list, validated API, or reference-domain lookup ensures that users can select only permitted codes. This directly supports Validity and improves Consistency across applications.
The rule should be linked to centrally governed Reference Data rather than hard-coded independently in each application. That allows changes to the permitted domain to be managed systematically.
Downstream cleansing remains useful for historical defects or external data, but it should not be the primary control when the organization controls the source-entry process.
DAMA's framework links Reference/Master Data Management to consistency of shared values and Chapter
13 to defining and monitoring Data Quality controls.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Preventive Controls; Validity; Data Creation; Chapter
10 - Reference Data; Controlled Domains.
NEW QUESTION # 22
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