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| Section | Weight | Objectives |
|---|---|---|
| Data Quality Strategy and Governance | 20% | - Integration with data governance - Data quality strategy and policy - Roles and responsibilities |
| Data Quality Assessment and Profiling | 25% | - Defining quality rules and metrics - Assessment methods and tools - Data profiling techniques |
| Tools, Techniques and Implementation | 20% | - Data quality tools and technologies - Data quality in lifecycle management - Business value and ROI |
| Data Quality Concepts and Principles | 15% | - Definition and importance of data quality - Data quality in DMBOK framework - Data quality dimensions |
| Data Quality Improvement | 20% | - Monitoring and continuous improvement - Root cause analysis - Data cleansing and standardization |
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NEW QUESTION # 87
One way of defining ethics is:
Answer: B
Explanation:
The correct principle is "Doing it right when no one is looking." The statement captures the essential distinction between ethical behaviour and mere compliance. Ethical conduct does not depend on observation, enforcement, or the probability of being caught; it requires responsible action because the action itself is appropriate. The answer wording is presented directly in the PDF on page 4.
DAMA-DMBOK2 treats data ethics as an essential component of professional Data Management because organizations routinely make choices about collecting, combining, analyzing, retaining, sharing, and monetizing data. A technically permissible activity may still create inappropriate effects on individuals or expose data to potential misuse.
Data ethics therefore extends beyond security and regulatory compliance. It requires consideration of the impact on people, potential for misuse, and economic value of data. Ethical handling also requires transparency concerning data provenance, intended use, quality limitations, and the consequences of decisions based on data.
Data Quality has an ethical dimension as well. Knowingly using inaccurate, incomplete, misleading, or poorly understood data in consequential decisions can harm customers and other stakeholders. Governance should therefore establish not only compliance controls but also principles for responsible use.
Reference Topics: DAMA-DMBOK2 Chapter 2 - Data Handling Ethics; Ethical Principles; Impact on People; Potential for Misuse; Chapter 13 - Reliable and Fit-for-Purpose Data.
NEW QUESTION # 88
A security mechanism that searches for customer bank account details in outgoing emails is achieving the goal of:
Answer: C
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 # 89
The main difference between a System of Record and a System of Reference is:
Answer: C
Explanation:
DAMA-DMBOK2 distinguishes the two concepts according to origination versus authoritative consumption.
A System of Record (SoR) is an authoritative system in which data is created, captured, or maintained according to defined rules and expectations. A System of Reference (SoRef) is an authoritative location from which consumers obtain reliable information for transactions or analysis even though that information may have originated elsewhere.
Therefore, option A expresses the essential distinction most accurately. For example, an operational CRM may be the System of Record for customer data because customer information is captured and maintained there. An MDM hub or enterprise data-sharing platform can subsequently become the System of Reference used by other applications, even though the original data was created in the CRM.
This distinction is important for Data Quality and lineage. Consumers need to know where values originated, where authoritative corrections are performed, which system distributes trusted values, and which stewardship process governs conflicts between sources. Metadata should therefore record source lineage, system roles, transformations, and authoritative status.
Option D is incorrect because "System of Record" and "System of Reference" do not correspond respectively to master data and reference data categories. Either architectural role can participate in managing different categories of enterprise data.
Reference Topics: DAMA-DMBOK2 Chapter 10 - System of Record and System of Reference; Trusted Sources; MDM Architecture; Metadata Lineage; Chapter 13 - Data Quality and Reference/Master Data Management.
NEW QUESTION # 90
Data Staging areas are populated from source databases using extract functions, transformed and then:
Answer: C
Explanation:
The conventional flow is Extract, Transform, and Load (ETL). Data is extracted from source systems, placed in a staging environment where required transformations are performed, and then loaded into the target Data Warehouse. Consequently, the transformed staging data is made ready and loaded into the Data Warehouse.
DAMA-oriented training material presents this exact progression.
The staging area provides a controlled environment in which source data can be integrated without imposing transformation workloads directly on operational systems. Typical activities include datatype conversion, standardization, code translation, matching, deduplication, validation, derivation, consolidation, and handling rejected records.
From a Data Quality perspective, staging is especially important because many quality controls can be applied before data reaches the analytical repository. Profiling can identify unexpected distributions, invalid values, missing records, duplicate entities, referential-integrity defects, and inconsistent reference values. Failed records can then be quarantined or remediated according to governed rules.
Although metrics and profiling may occur in staging, they are supporting activities rather than the final destination in the ETL sequence. Likewise, metadata repositories document the transformation rather than serving as the main target for the transformed business data.
Reference Topics: DAMA-DMBOK2 - Data Warehousing and Business Intelligence; ETL; Staging Areas; Data Integration; Chapter 13 - Profiling, Cleansing, Standardization and Validation.
NEW QUESTION # 91
The goals of data storage and operations are:
Answer: A
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 # 92
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