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| Section | Objectives |
|---|---|
| Topic 1: Data Quality Assessment | - Data Profiling Techniques - Data Quality Metrics and KPIs |
| Topic 2: Data Quality Improvement | - Root Cause Analysis - Data Cleansing Methods |
| Topic 3: Data Quality Operations | - Data Quality Tools and Technologies - Monitoring and Reporting |
| Topic 4: Data Quality Fundamentals | - Data Quality Dimensions (Accuracy, Completeness, Consistency, Timeliness, Validity, Uniqueness) - Definition of Data Quality |
| Topic 5: Data Quality Management | - Roles and Responsibilities (Data Stewardship) - Data Quality Governance |
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NEW QUESTION # 109
Three source systems provide different telephone numbers for the same customer. An MDM hub selects one number according to approved source-priority rules. This is an example of:
Answer: B
Explanation:
The process is Survivorship. In Master Data Management, survivorship determines which value should become the preferred or authoritative representation when multiple source records contain conflicting values for the same attribute.
Rules may prioritize particular systems, use recency, trust scores, verification status, completeness, or combinations of factors. For example, a verified customer-service update might outrank an older marketing- system telephone number.
Survivorship occurs after or in conjunction with matching and entity resolution. The organization first determines that records from different sources represent the same real-world customer and then determines which attribute values should populate the mastered representation.
The process requires governance because the "best" value is a business decision, not merely a technical one.
Data Stewards should approve the rules, and metadata should record source priority, rule logic, and lineage.
DAMA's framework positions Reference and Master Data Management as the discipline responsible for ensuring consistent core entities across the organization.
Reference Topics: DAMA-DMBOK2 Chapter 10 - Master Data Management; Matching; Survivorship; Authoritative Values; Chapter 13 - Consistency and Accuracy.
NEW QUESTION # 110
Who does the DMBoK consider to be generally responsible for developing business glossary content?
Answer: C
Explanation:
DAMA-DMBOK2 assigns primary responsibility for business glossary content to Business Data Stewards.
Business Data Stewards are typically subject-matter experts who understand how data is defined, created, interpreted, and consumed within their business domain. DMBOK2 explicitly states that Data Stewards are generally responsible for business glossary content and identifies Business Data Stewards as professionals who work with stakeholders to define and control data.
A business glossary is not simply a technical dictionary. It establishes agreed business terminology, definitions, synonyms, business rules, responsible stewards, and relationships between business concepts.
This makes stewardship involvement essential because definitions must reflect operational and business meaning rather than merely database structures.
Data Architects may contribute candidate definitions and structural context from subject-area and conceptual models, but they do not generally own the business meaning. Business users provide valuable input, while Coordinating Data Stewards help reconcile definitions across domains, yet the normal accountability remains with Business Data Stewards.
This relationship is particularly important for Data Quality. Quality rules depend on unambiguous definitions of data elements. If "Customer," "Active Account," or "Order Date" has inconsistent meanings, measurements of completeness, accuracy, and validity cannot be consistently interpreted.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Develop a Business Glossary; Business Data Stewardship; Metadata Management; Chapter 13 - Data Quality Rules and Business Definitions.
NEW QUESTION # 111
An application that attempts to predict future outcomes through probability estimates is called:
Answer: E
Explanation:
Predictive analytics applies statistical, mathematical or machine-learning models to historical and current data in order to estimate future events, behaviours or outcomes. DAMA's treatment of analytics positions predictive analytics as a use of managed data in which patterns discovered from existing information are used to project what is likely to occur rather than merely describe what has already occurred. DMBOK2 explicitly places predictive analytics among advanced uses of data and notes the evolution of Business Intelligence from retrospective analysis toward predictive methods.
Probability estimation is therefore the distinguishing feature in this question. Descriptive analytics summarizes historical conditions-what happened. Predictive analytics estimates what may happen and commonly produces probabilities, scores, classifications or forecasts. "Reactive analytics," "dimensional analytics," and "just-in-time reporting" do not describe this statistical forecasting function.
Data Quality is fundamental to predictive performance. Missing values, inaccurate labels, duplicate entities, inconsistent categories, stale observations and biased source populations can materially distort estimated probabilities. Profiling should therefore assess distributions, completeness, outliers and consistency before modelling. Metadata must preserve definitions, lineage and transformation logic, while governed Master and Reference Data provide consistent classifications across training and operational datasets.
Predictive outputs themselves can also be monitored statistically for unexpected distribution shifts and unreasonable patterns.
Reference Topics: DAMA-DMBOK2 - Data Warehousing and Business Intelligence; Predictive Analytics; Chapter 13 - Profiling, Statistical Quality Control, Reasonableness, Accuracy and Completeness.
NEW QUESTION # 112
A Data Quality rule states that every active supplier must have a valid tax identifier. Who should approve whether the rule is mandatory for the Supplier domain?
Answer: B
Explanation:
Whether the rule is mandatory is a business governance decision and should be approved through the appropriate Data Owner, Data Steward, or governance authority responsible for the Supplier domain.
A developer can implement a database constraint, but technical implementation does not establish business policy. The organization must determine whether every active supplier genuinely requires a tax identifier, whether exceptions exist by jurisdiction or supplier type, and what consequences should follow when the rule is violated.
The approved rule should be documented as metadata and linked to the relevant business term, physical fields, authoritative sources, threshold, and responsible steward.
This illustrates the separation between governance and execution. Governance defines and authorizes the requirement; operational and technical teams implement the validation and monitoring.
DAMA's public framework places Governance across the Data Management knowledge areas and Metadata Management as the mechanism for maintaining definitions, lineage, and usage context.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Governance and Stewardship; Chapter 13 - Data Quality Rules; Metadata Management; Supplier Master Data.
NEW QUESTION # 113
Database monitoring tools measure key database metrics, such as:
Answer: B
Explanation:
DAMA-DMBOK2 identifies capacity, availability, cache performance, and user statistics as representative metrics captured by database-monitoring tools. Database monitoring is an operational control mechanism used by Database Administrators and platform teams to understand whether database infrastructure is available, adequately sized, responsive, and being used as expected. DMBOK2 explicitly describes monitoring tools as automating the observation of metrics such as capacity, availability, cache performance, and user statistics.
Capacity metrics indicate resource consumption and future growth requirements. Availability measures whether data services remain accessible when required. Cache-performance measures help identify inefficient access patterns and bottlenecks, while user statistics provide information about workload and database consumption.
The other choices mix design concepts, CRUD operations, or Data Quality concepts with operational monitoring measures. For example, normalization is a modelling technique rather than a routine runtime performance metric. Similarly, "create, read, update, delete" describes basic data operations rather than monitoring indicators.
Although database performance and Data Quality are distinct disciplines, poor operational performance can affect Data Quality dimensions such as timeliness and availability. Monitoring therefore supports the technical environment in which governed, reliable information is delivered to users and applications.
Reference Topics: DAMA-DMBOK2 - Data Storage and Operations; Database Operations; Capacity and Availability Management; Monitoring; Chapter 13 - Timeliness and Operational Fitness for Purpose.
NEW QUESTION # 114
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