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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 Assessment | - Data Profiling Techniques - Data Quality Metrics and KPIs |
| Topic 3: Data Quality Operations | - Monitoring and Reporting - Data Quality Tools and Technologies |
| Topic 4: Data Quality Management | - Roles and Responsibilities (Data Stewardship) - Data Quality Governance |
| Topic 5: Data Quality Improvement | - Data Cleansing Methods - Root Cause Analysis |
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NEW QUESTION # 116
A Data Quality team has identified 500 defects across several domains. Which factor should have the strongest influence on remediation priority?
Answer: C
Explanation:
Remediation should principally be prioritized according to business impact and risk. Not all defects have equivalent consequences, even when they occur at similar frequencies.
A defect affecting regulatory reporting, customer payments, safety-critical operations, executive reporting, or high-value master data may require immediate remediation. A larger number of defects affecting a low- impact optional field may legitimately receive lower priority.
A robust prioritization model may consider financial loss, regulatory exposure, operational disruption, customer impact, reputational damage, number of dependent systems, recurrence rate, remediation cost, and whether a Critical Data Element is involved.
This risk-based approach prevents Data Quality programs from becoming simple defect-count reduction exercises. The objective is not merely to maximize the number of corrected records but to improve fitness for purpose where poor data creates material consequences.
Governance should approve prioritization criteria and resolve conflicts where different business areas assign different importance to the same issue. Metadata and lineage provide evidence about downstream dependencies and affected processes.
DAMA's revision of Chapter 13 adds a clearer Critical Data Element concept and clarifies responsibility within the Data Quality Improvement Lifecycle, reinforcing risk-based prioritization.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Issue Prioritization; Business Impact; Critical Data Elements; Risk; Remediation.
NEW QUESTION # 117
A business driver for the creation of a Data Quality Management program is:
Answer: E
Explanation:
A primary business driver for establishing a Data Quality Management program is to reduce the risks and costs associated with poor-quality data. DAMA-DMBOK2 treats low-quality data as a direct source of operational cost and organizational risk. The Chapter 13 business drivers include increasing the value of organizational data, reducing risks and costs caused by poor quality, improving efficiency and productivity, and protecting organizational reputation.
Poor-quality data generates both visible and hidden costs. Examples include rework, manual correction, incorrect invoices, failed customer interactions, regulatory exposure, inappropriate decisions, integration delays, missed opportunities, and reputational damage. DMBOK2 further emphasizes that low-quality data represents cost and risk rather than value, making Data Quality management necessary throughout the data lifecycle.
A formal DQ program addresses these consequences through defined requirements, profiling, measurement, root-cause analysis, corrective and preventive action, monitoring, and governance. The purpose is therefore broader than making the DQ team efficient or supporting a specific technology initiative such as warehouse consolidation.
The economic and risk-reduction argument also provides the strongest basis for executive sponsorship because improvements can be linked directly to measurable business outcomes.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Business Drivers; Costs of Poor Data Quality; Risk Reduction; Data Quality Program; Data Quality Lifecycle.
NEW QUESTION # 118
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: E
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 # 119
Discovering and documenting metadata about physical data assets provides:
Answer: E
Explanation:
Discovering and documenting metadata about physical data assets provides visibility into how data moves and is transformed between systems. This is a core function of technical metadata and data lineage.
DAMA-DMBOK2 states that technical metadata describes the technical characteristics of data, the systems that store it, and the processes that move it within and between systems. Examples include physical table and column names, ETL job information, source-to-target mappings, and lineage documentation with upstream and downstream impact information. The certification question therefore points directly to option D; the same interpretation is independently reflected in published CDMP-oriented material.
This capability is critical to Data Quality because a defect observed in a report or downstream application may not originate in the current system. It may have been introduced through extraction logic, transformation rules, reference-data conversion, aggregation, or loading.
Documented physical metadata allows analysts to trace the affected value backward, identify its source, inspect each transformation, and isolate the point at which the defect occurred. It also supports change-impact analysis: modifying a source field or mapping can reveal which downstream assets may be affected.
Metadata therefore provides the evidence required for systematic root-cause analysis rather than symptom- based correction.
Reference Topics: DAMA-DMBOK2 Metadata Management - Technical Metadata; Source-to-Target Mapping; Data Lineage; Data Integration and Interoperability; Chapter 13 - Root-Cause Analysis and Quality Monitoring.
NEW QUESTION # 120
A customer table contains 50,000 records. Mandatory Tax Identification Number values are missing from
2,500 records. Which metric most directly measures the defect?
Answer: C
Explanation:
The defect is measured using Completeness. Completeness evaluates whether all required records or values that should be present are actually present. In this scenario, the mandatory Tax Identification Number is absent from 2,500 of 50,000 customer records.
A straightforward attribute-level completeness metric would be calculated as the number of populated required values divided by the number expected. Therefore, 47,500 of 50,000 records contain the required value, producing a completeness result of 95%.
Completeness does not establish that populated values are accurate. A record may contain a Tax Identification Number and therefore pass the completeness rule while containing the wrong number. This separation between completeness and accuracy is essential when designing Data Quality scorecards. DAMA-aligned guidance defines completeness as the presence of required values and explicitly distinguishes it from factual correctness.
Governance should also determine whether the attribute is genuinely mandatory for every customer type.
Quality rules must reflect business applicability rather than blindly requiring every field for every record.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Completeness; Data Quality Metrics; Business Rules; Critical Data Elements; Scorecards.
NEW QUESTION # 121
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