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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Quality Assessment and Profiling | 25% | - Data profiling techniques - Assessment methods and tools - Defining quality rules and metrics |
| 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 Improvement | 20% | - Data cleansing and standardization - Monitoring and continuous improvement - Root cause analysis |
| 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 Strategy and Governance | 20% | - Data quality strategy and policy - Integration with data governance - Roles and responsibilities |
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NEW QUESTION # 115
A search engine database is populated by a web crawler or spider software that usually processes as:
Answer: D
NEW QUESTION # 116
Which of the following is a reason why organisations do not dispose of non-value-adding information?
Answer: B
Explanation:
The correct answer is storage is cheap and easily expanded. DAMA-DMBOK2 discusses retention and disposal as important lifecycle-management responsibilities. Although information that no longer provides business, legal, regulatory, historical, or evidentiary value should normally be disposed of according to approved retention policies, organizations often postpone disposal because modern storage appears inexpensive and technically easy to expand.
This reasoning is deceptive. The acquisition cost of storage is only one component of total information- management cost. Retaining unnecessary information also increases backup requirements, recovery time, discovery obligations, privacy exposure, security risk, metadata-management effort, migration complexity, and the volume of information that must be governed. DMBOK2 therefore stresses that non-value-adding information should not be retained merely because storage capacity is readily available.
From a Data Quality perspective, excessive retention also increases the population of obsolete, redundant, and potentially inconsistent information. This makes profiling, lineage analysis, master-data reconciliation, and authoritative-source identification more difficult.
A sound governance program consequently combines retention schedules, legal requirements, metadata classification, defensible disposal procedures, and clear accountability so that data is retained for legitimate reasons rather than technological convenience.
Reference Topics: DAMA-DMBOK2 - Document and Content Management; Retention and Disposal; Information Lifecycle; Data Governance; Chapter 13 - Data Quality and Obsolete Data.
NEW QUESTION # 117
Multiple data warehouses, lakes, and swamps are an indicator that the organisation values:
Answer: A
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 # 118
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 # 119
A financial transaction is captured correctly at 9:00 AM but does not become available to the fraud- monitoring system until 6:00 PM, although the business requirement is availability within five minutes.
Which Data Quality dimension is primarily violated?
Answer: B
Explanation:
The primary failure is Timeliness. The transaction may be completely accurate and complete, but it is not available within the period required by the consuming business process.
Timeliness evaluates whether data is available when needed for its intended use. The relevant threshold must therefore come from the business requirement rather than from an arbitrary technical target. In this scenario, the fraud-monitoring process requires the transaction within five minutes, while delivery occurs approximately nine hours later.
The root cause could exist in extraction frequency, integration queues, batch processing, network delays, source-system availability, or downstream ingestion. Lineage and operational metadata should be used to identify where the latency occurs.
Timeliness must also be distinguished from Currency. Currency asks whether information reflects a sufficiently recent real-world state; Timeliness asks whether data is delivered or available within the required period. A current transaction that arrives too late can therefore fail Timeliness even though the underlying value accurately represented reality when captured.
DAMA's revised DMBOK2 Chapter 13 recognizes both Timeliness and Currency as separate standard dimensions.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Timeliness; Currency; Data Quality Requirements; Data Integration; Operational Monitoring.
NEW QUESTION # 120
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