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| Section | Weight | Objectives |
|---|---|---|
| Data Quality Improvement | 20% | - Data Enrichment - Standardization - Validation Rules - Data Cleansing Techniques |
| Data Quality Governance | 15% | - Data Stewardship - Data Quality Roles and Responsibilities - Policies and Procedures - Continuous Monitoring |
| Data Quality Assessment and Measurement | 25% | - Profiling Techniques - Benchmarking - Root Cause Analysis - Metrics and KPIs |
| Data Quality Frameworks and Standards | 20% | - DAMA DMBOK Framework - Other Industry Standards - ISO 8000 (Data Quality) |
| Data Quality Fundamentals | 20% | - Definition of Data Quality - Data Quality in the Data Lifecycle - Dimensions of Data Quality (completeness, accuracy, consistency, timeliness, validity) |
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NEW QUESTION # 91
The implementation of a 'Master Data Repository', which is integrated across the enterprise, is an example of which integration approach?
Answer: D
Explanation:
An enterprise Master Data Repository serving multiple applications is a classic Hub-and-Spoke integration pattern. In this architecture, shared information is consolidated physically or virtually in a central hub, while participating applications interact with that hub rather than building a separate direct interface to every other application.
DAMA-DMBOK2 explicitly identifies Master Data Management hubs, Data Warehouses, Data Marts, and Operational Data Stores as familiar examples of data hubs. The hub-and-spoke model reduces the proliferation of point-to-point interfaces and can provide a consistent enterprise view of shared data.
This architecture is particularly appropriate for MDM because customer, product, supplier, location, or other master entities often need to be standardized, matched, governed, and redistributed across many systems. The central hub can apply survivorship rules, reference mappings, stewardship decisions, and quality controls before distributing trusted values.
Replication and Change Data Capture are techniques for moving or detecting changes in data, but neither describes the overall interaction topology. Publish-subscribe may operate alongside an MDM hub as a distribution mechanism, but the enterprise repository itself represents the hub within a hub-and-spoke architecture.
Reference Topics: DAMA-DMBOK2 Chapter 8 - Hub-and-Spoke; Integration Interaction Models; Chapter
10 - Master Data Management; Enterprise Master Data Hub; Data Quality Consistency.
NEW QUESTION # 92
A control chart shows a stable defect rate for several weeks, followed by one point far outside the established control limits. This most strongly indicates:
Answer: D
Explanation:
A point materially outside established control limits is a classic signal of special-cause variation. Statistical Process Control distinguishes routine variation inherent in a stable process from unusual variation introduced by an identifiable event or change.
When a Data Quality process normally produces a stable percentage of invalid records and a sudden observation lies beyond the expected statistical range, investigation should focus on what changed. Possible causes include a new source system, software deployment, changed mapping, revised business rule, unusual batch, or manual processing error.
The current DMBOK2 Chapter 13 revision retains Statistical Process Control concepts while reorganizing them into supporting material and explicitly maps Shewhart/Deming improvement stages to Data Quality processes.
The signal does not prove what caused the defect, nor does it establish that performance has improved or deteriorated permanently. It indicates that the observed variation is sufficiently unusual to justify analysis.
Quality teams should preserve the evidence, correlate the timing with system and process changes, use lineage to identify affected sources, and investigate before adjusting the process unnecessarily.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Statistical Process Control; Control Charts; Common Cause; Special Cause; Continuous Improvement.
NEW QUESTION # 93
An effective Data Governance communication program should include the following:
Answer: E
Explanation:
An effective Data Governance communication program should employ multiple complementary communication mechanisms, making All answers correct. Governance changes how people define, create, use, approve, and resolve issues with data; consequently, sustained adoption requires more than publishing policies.
Regular newsletters keep stakeholders aware of progress, decisions, metrics, and upcoming activities. A Data Governance Portal provides a persistent location for policies, standards, stewardship information, glossaries, issue processes, and supporting materials. Custom training develops the capabilities required for individuals to understand their specific governance responsibilities. Informal networking events help establish relationships across business and technical groups, which is particularly important when resolving data ownership and definition conflicts.
DAMA-DMBOK2 treats communication and organizational change as core implementation considerations because governance depends on participation across functions rather than on a single technical team.
Published CDMP material for this item identifies the combined response-newsletters, portal, training, and networking-as the intended answer.
For Data Quality, communication ensures that quality definitions, issue-management procedures, stewardship responsibilities, thresholds, and remediation decisions are understood and consistently applied.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Governance Communications; Organizational Change; Training; Data Governance Portal; Stewardship Engagement; Chapter 13 - Data Quality Culture.
NEW QUESTION # 94
Which of the following is a reason why organisations do not dispose of non-value-adding information?
Answer: D
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 # 95
An address-validation service reports that 96% of customer addresses conform to an approved postal format.
What does this metric demonstrate most directly?
Answer: B
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 # 96
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