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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Quality Improvement | 20% | - Data cleansing and standardization - Root cause analysis - Monitoring and continuous improvement |
| Topic 2: Tools, Techniques and Implementation | 20% | - Business value and ROI - Data quality tools and technologies - Data quality in lifecycle management |
| Topic 3: Data Quality Strategy and Governance | 20% | - Roles and responsibilities - Data quality strategy and policy - Integration with data governance |
| Topic 4: Data Quality Concepts and Principles | 15% | - Data quality in DMBOK framework - Data quality dimensions - Definition and importance of data quality |
| Topic 5: Data Quality Assessment and Profiling | 25% | - Defining quality rules and metrics - Data profiling techniques - Assessment methods and tools |
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NEW QUESTION # 79
Well executed metadata management enables:
Answer: A
Explanation:
The fundamental outcome of well-executed Metadata Management is a consistent understanding of data resources. DAMA-DMBOK2 states this directly: effective Metadata Management enables consistent understanding of data resources and supports more efficient development across organizational boundaries.
Metadata supplies context that raw data cannot provide by itself. It explains business meaning, technical structures, valid domains, calculations, transformations, ownership, lineage, systems of record, access restrictions, known quality issues, and update schedules. DMBOK2 also identifies Data Quality rules and measurement results as forms of business metadata.
Consequently, Metadata Management is broader than database design, dimensional reporting, authorization, or Data Modeling. Each of those activities consumes metadata, but none represents its complete objective.
This capability is essential to Data Quality. A dataset cannot be meaningfully evaluated for fitness for purpose unless users know what its fields mean, where the data originated, how values were transformed, which business rules apply, and which source is authoritative. Metadata therefore provides the semantic and technical framework required to define and interpret Data Quality measurements consistently.
DAMA's current DMBOK2 revision also explicitly strengthens the relationship between Data Quality and Metadata Management.
Reference Topics: DAMA-DMBOK2 Metadata Management - Goals and Principles; Business Metadata; Technical Metadata; Data Lineage; Chapter 13 - Data Quality and Metadata Management.
NEW QUESTION # 80
Profiling shows that whenever Account_Status is "Closed", Closure_Date is normally populated. However,
4% of closed accounts have a null Closure_Date. What type of profiling has revealed this issue?
Answer: A
Explanation:
This is cross-column dependency profiling. The issue cannot be identified merely by counting null values in Closure_Date because some nulls may be legitimate for active accounts. The defect becomes meaningful only when the relationship between two attributes is examined.
The implied business rule is: if Account_Status = Closed, then Closure_Date must be populated. Dependency profiling examines relationships among values and can reveal conditional completeness, inconsistent combinations, functional dependencies, and other multi-attribute patterns.
The next step is to confirm the inferred pattern with business stakeholders rather than automatically converting it into an enforced rule. There may be exceptional account categories where a closed status legitimately lacks a closure date.
Once approved, the rule should be documented as metadata, assigned to a Data Steward, measured against an agreed threshold, and monitored. Root-cause investigation can then determine whether missing dates arise from source-screen design, process bypass, conversion defects, or integration failures.
DAMA's revised Chapter 13 places Data Quality techniques in a more practical sequence and strengthens their linkage with Data Modeling and Metadata Management.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Profiling; Dependency Analysis; Completeness; Business Rules; Metadata Management.
NEW QUESTION # 81
The goals of data storage and operations are:
Answer: B
Explanation:
DAMA-DMBOK2 states three principal goals for Data Storage and Operations: managing data availability throughout the lifecycle, ensuring the integrity of data assets, and managing the performance of data transactions. Option A corresponds directly to those goals.
Availability means that required data and database services are accessible when business processes need them. This requires capacity planning, backup and recovery, business continuity, monitoring, and resilient infrastructure.
Integrity means protecting data assets from corruption or inappropriate alteration and maintaining reliable technical structures. Operational controls such as recovery procedures, transaction management, and controlled changes contribute directly to integrity.
Performance concerns the efficiency and responsiveness of database transactions and related storage operations. Capacity, cache behaviour, indexing, workload patterns, and system resources must therefore be monitored.
Glossary management belongs primarily to Metadata Management, while authorized access is principally a Data Security responsibility. User experience is not one of the stated core objectives for this knowledge area.
The relationship to Data Quality is direct: technically corrupted or unavailable information cannot be fit for purpose even when its business definitions are correct.
Reference Topics: DAMA-DMBOK2 Chapter 6 - Goals and Principles of Data Storage and Operations; Availability; Integrity; Performance; Chapter 13 - Data Reliability and Timeliness.
NEW QUESTION # 82
The ethics of data handling, center on several core concepts. They are:
Answer: C
Explanation:
DAMA-DMBOK2 organizes Data Handling Ethics around three fundamental considerations: impact on people, potential for misuse, and the economic value of data. The text explains that data frequently represents individuals and is used in decisions that materially affect them; consequently, organizations have an ethical responsibility to ensure that data is reliable and appropriately managed. It also recognizes that data can be intentionally or unintentionally misused and that its economic value creates questions concerning ownership, access, rights, and permitted use.
These concepts directly intersect with Data Quality. Incorrect or misleading data may result in decisions that adversely affect customers, employees, patients, citizens, or other stakeholders. For that reason, accuracy, completeness, transparency, provenance, and appropriate interpretation are not merely technical characteristics-they can have ethical consequences.
Governance operationalizes these ethical principles through policies, decision rights, stewardship responsibilities, access rules, monitoring, and escalation procedures. Metadata Management supports transparency by documenting meaning, provenance, classification, and permitted use. Master Data Management also has an ethical dimension because consolidated person or customer records may create significant consequences when matching or identity-resolution errors occur.
Privacy and security are important controls, but they do not constitute DAMA's complete set of core ethical concepts.
Reference Topics: DAMA-DMBOK2 Chapter 2 - Data Handling Ethics; Impact on People; Potential for Misuse; Economic Value of Data; Chapter 13 - Accuracy, Reliability and Governance.
NEW QUESTION # 83
An address-validation service reports that 96% of customer addresses conform to an approved postal format.
What does this metric demonstrate most directly?
Answer: A
Explanation:
The metric demonstrates Validity because it measures whether address values conform to a defined format or standard.
Validity checks whether data satisfies specified rules concerning datatype, pattern, permitted values, ranges, or structures. Postal formatting rules might require approved postcode structure, valid country-code syntax, or required address components.
The metric does not prove that the address belongs to the correct customer. A perfectly formatted address may still be factually wrong. That would be an Accuracy problem.
DAMA-aligned government guidance defines validity in terms of conformity to expected format, type, range, and other predefined rules.
A mature address-quality program may combine validity with verification against authoritative postal reference data, duplication checks, completeness rules, and currency monitoring.
Quality scorecards should therefore label the metric precisely rather than calling the result a generic "accuracy score." Reference Topics: DAMA-DMBOK2 Chapter 13 - Validity; Accuracy; Data Quality Metrics; Address Quality; Reference Data.
NEW QUESTION # 84
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