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| Section | Objectives |
|---|---|
| Topic 1: Data Quality Assessment | - Data Profiling Techniques - Data Quality Metrics and KPIs |
| Topic 2: Data Quality Operations | - Monitoring and Reporting - Data Quality Tools and Technologies |
| Topic 3: Data Quality Improvement | - Data Cleansing Methods - Root Cause Analysis |
| Topic 4: Data Quality Fundamentals | - Definition of Data Quality - Data Quality Dimensions (Accuracy, Completeness, Consistency, Timeliness, Validity, Uniqueness) |
| Topic 5: Data Quality Management | - Roles and Responsibilities (Data Stewardship) - Data Quality Governance |
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質問 # 50
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?
正解：B
解説：
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.
質問 # 51
The main difference between a System of Record and a System of Reference is:
正解：E
解説：
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.
質問 # 52
A retail system accepts an order for 750,000 identical office chairs from an individual consumer. The value passes all datatype, domain, and mandatory-field checks. Which additional Data Quality dimension should detect the anomaly?
正解：A
解説：
The appropriate dimension is Reasonableness. A value may comply with technical and business-domain constraints while still being implausible in its operating context. An individual customer purchasing 750,000 office chairs is technically possible, but it is sufficiently unusual that it should trigger review.
Reasonableness controls evaluate whether values and combinations of values fall within credible expectations. These controls often depend on business context, historical behaviour, peer comparisons, statistical limits, or relationships between multiple fields.
The current DMBOK2 revision standardizes the term Reasonableness within its nine standard dimensions.
The broader DMBOK2 dimension framework links reasonableness to whether data should be regarded as credible within the operational context.
Useful controls might compare order quantity against customer type, historical maximums, product availability, typical basket size, or statistical deviation from normal purchasing behaviour.
Reasonableness rules are especially valuable for identifying errors that conventional validation cannot detect.
However, unusual data should not automatically be treated as incorrect; it should usually be flagged for investigation.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Reasonableness; Statistical Validation; Exception Detection; Business Rules.
質問 # 53
Data governance represents:
正解：B
解説：
DAMA-DMBOK2 explicitly states that Data Governance represents an inherent separation of duty between oversight and execution. It illustrates the principle through an analogy with financial governance: an auditor exercises oversight over financial processes without personally executing financial management. Similarly, Data Governance ensures that data is properly managed but does not itself perform every operational Data Management activity.
This separation is fundamental because governance must retain sufficient independence to establish rules, monitor compliance, resolve conflicts, assign accountability, and evaluate whether operational teams are managing data according to approved policies and standards.
Option B describes activities in which governance and Data Quality practitioners may collaborate, but it is not the defining governance principle. Option D is also incorrect because federated governance is only one possible operating model; DMBOK2 also recognizes centralized and replicated approaches. Option E describes an organizational implementation, not the conceptual distinction.
Within Data Quality Management, this means that governance may approve quality policies, Critical Data Elements, acceptable thresholds, stewardship structures, and escalation procedures, while DQ analysts and operational teams perform profiling, cleansing, monitoring, and remediation.
Maintaining this division reduces conflicts of interest and establishes accountability for whether data- management activities achieve agreed business objectives.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Essential Concepts; Oversight versus Execution; Governance Operating Models; Chapter 13 - Data Quality Governance and Operational Responsibilities.
質問 # 54
A DMZ is bordered by 2 firewalls. These are between the DMZ and the:
正解：B
解説：
A Demilitarized Zone (DMZ) is a controlled network segment positioned between an organization's trusted internal environment and an external, untrusted environment such as the Internet. In the classic architecture described in DAMA-DMBOK2, the DMZ is separated from the Internet by an external firewall and from internal organizational systems by another firewall. Therefore, the correct answer is Internet and internal systems.
DMBOK2 describes the DMZ as an area on the organization's perimeter, with firewall protection separating it from the organization and firewall protection between the DMZ and the Internet. It is commonly used for services or transient data exchanges that must interact with external parties without providing direct access to internal systems.
This arrangement implements defense in depth. Public-facing services can operate in the DMZ while internal databases and sensitive systems remain behind an additional security boundary. A compromise of a DMZ host should therefore not automatically expose the internal network.
For Data Quality, security architecture is relevant because unauthorized modification or corruption can compromise integrity and accuracy. Protecting interfaces and repositories supports trustworthy data processing as well as confidentiality and availability.
Reference Topics: DAMA-DMBOK2 Chapter 7 - Data Security; Network Security; Perimeter; DMZ; Firewalls; Integrity Protection.
質問 # 55
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