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| Section | Weight | Objectives |
|---|---|---|
| Data Quality Improvement | 20% | - Data Cleansing Techniques - Standardization - Validation Rules - Data Enrichment |
| Data Quality Fundamentals | 20% | - Dimensions of Data Quality (completeness, accuracy, consistency, timeliness, validity) - Definition of Data Quality - Data Quality in the Data Lifecycle |
| Data Quality Governance | 15% | - Policies and Procedures - Data Quality Roles and Responsibilities - Data Stewardship - Continuous Monitoring |
| Data Quality Frameworks and Standards | 20% | - ISO 8000 (Data Quality) - DAMA DMBOK Framework - Other Industry Standards |
| Data Quality Assessment and Measurement | 25% | - Benchmarking - Metrics and KPIs - Root Cause Analysis - Profiling Techniques |
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NEW QUESTION # 29
Three common interaction models for data integration are:
Answer: C
Explanation:
DAMA-DMBOK2 identifies Point-to-Point, Hub-and-Spoke, and Publish-Subscribe as three common interaction models used to transfer data between systems.
In Point-to-Point, one system communicates directly with another. It is straightforward for a small number of systems but becomes difficult to maintain as interfaces proliferate. In Hub-and-Spoke, systems exchange information through a central hub, reducing direct dependencies and supporting greater consistency. In Publish-Subscribe, producing systems publish data while interested consumers subscribe to the relevant data service or event stream. DMBOK2 presents these three patterns explicitly within Data Integration and Interoperability.
The choice of pattern affects Data Quality as well as architecture. Point-to-point designs can create inconsistent transformations when several applications independently consume the same source. Hub-and- spoke designs can centralize transformations and reference mappings. Publish-subscribe supports scalable distribution but requires controlled message definitions and metadata so that every subscriber interprets data consistently.
DAMA therefore recommends choosing an interaction model according to functional, performance, latency, support, and governance requirements rather than allowing integration patterns to emerge independently.
Reference Topics: DAMA-DMBOK2 Chapter 8 - Data Integration and Interoperability; Interaction Models; Point-to-Point; Hub-and-Spoke; Publish-Subscribe; Canonical Models.
NEW QUESTION # 30
A goal of reference and master data management is for data to ensure shared data is:
Answer: D
Explanation:
DAMA-DMBOK2 explicitly identifies a core goal of Reference and Master Data Management as ensuring that shared Master and Reference Data is complete, consistent, current, and authoritative across organizational processes.
Complete means required shared entities and attributes are sufficiently populated for their intended use.
Consistent means equivalent data has compatible meaning and representation wherever it is consumed.
Current means the information reflects an acceptably recent state. Authoritative means the organization recognizes a trusted source or governed process for determining the accepted value.
These characteristics are particularly important because Master and Reference Data is reused widely. An incorrect Product classification, Customer identifier, Country code, or Supplier status can therefore propagate defects across many systems and business processes.
DAMA also emphasizes that shared Master and Reference Data belongs to the organization rather than to a single application or department. This creates a strong requirement for enterprise stewardship and governance.
Reference and Master Data Management consequently interacts directly with Chapter 13. MDM can consolidate and distribute shared data, but it does not guarantee quality automatically. Matching, survivorship, validation, standardization, stewardship, and continuous monitoring are required to ensure the resulting records remain trustworthy.
Reference Topics: DAMA-DMBOK2 Chapter 10 - Reference and Master Data Management Goals; Shared Data; Authoritative Sources; Stewardship; Chapter 13 - Completeness, Consistency and Currency.
NEW QUESTION # 31
Following the rollout of a data issue process, there have been no issues recorded in the first month. The reason for this might be:
Answer: D
Explanation:
A complete absence of reported issues immediately after introducing an enterprise Data Issue process is more likely to indicate lack of confidence in the governance process than genuinely flawless enterprise data. The certification material explicitly identifies lack of credibility in the process's ability to effect change as the plausible explanation.
Effective issue management depends on organizational trust. Employees need to believe that documenting an issue will lead to triage, ownership, escalation, root-cause investigation, remediation, and appropriate communication. If previous problems disappeared into a queue without action-or if raising defects creates organizational friction-users may simply stop reporting them.
DAMA-DMBOK2 treats Data Governance implementation as an organizational-change challenge rather than a purely procedural exercise. Governance must demonstrate authority, responsiveness, transparency, and measurable outcomes to establish credibility.
For Data Quality, issue volumes must also be interpreted carefully. "Zero issues reported" is not equivalent to
"zero defects." Complementary evidence should come from profiling, automated monitoring, quality metrics, user feedback, reconciliation, and operational outcomes.
Management should investigate reporting barriers, communicate resolved cases, establish clear escalation paths, and demonstrate that identified problems produce tangible improvements.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Governance Adoption and Credibility; Data Issue Management; Chapter 13 - Issue Identification, Escalation, Root-Cause Analysis and Remediation.
NEW QUESTION # 32
During initial profiling of a Country_Code attribute, the analyst wants to identify unexpected values such as
"UKK", "ENG", and blanks. Which profiling output is most useful?
Answer: A
Explanation:
A frequency distribution is the most useful profiling output because it shows each distinct value and how often it occurs. For a controlled attribute such as Country_Code, this quickly reveals unexpected values, misspellings, blanks, obsolete codes, and disproportionately common defaults.
Data profiling is an analytical process used to examine actual data populations and determine their structure, content, patterns, distributions, and potential defects. Frequency analysis is particularly valuable for categorical attributes because it provides evidence about the real values present rather than relying only on declared metadata.
The result can be compared with an authoritative Reference Data domain. Values outside the approved code set become validity exceptions, while unusual frequencies may indicate mapping defects or default-value problems.
This is also where Metadata Management and Reference Data Management interact strongly with Data Quality. Metadata identifies the expected definition and domain; Reference Data provides the authorized codes; profiling determines whether operational values actually conform.
A data model can show that Country_Code exists and perhaps its datatype, but it does not reveal the actual value distribution currently stored in production.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Profiling; Frequency Analysis; Validity; Reference Data; Metadata Management.
NEW QUESTION # 33
A business case for adding a new master data management solution is dependent on achieving greater value from:
Answer: C
NEW QUESTION # 34
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