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| Section | Weight | Objectives |
|---|---|---|
| Data Quality Concepts and Principles | 15% | - Definition and importance of data quality - Data quality in DMBOK framework - Data quality dimensions |
| Tools, Techniques and Implementation | 20% | - Data quality tools and technologies - Data quality in lifecycle management - Business value and ROI |
| Data Quality Strategy and Governance | 20% | - Data quality strategy and policy - Roles and responsibilities - Integration with data governance |
| Data Quality Improvement | 20% | - Data cleansing and standardization - Root cause analysis - Monitoring and continuous improvement |
| Data Quality Assessment and Profiling | 25% | - Data profiling techniques - Defining quality rules and metrics - Assessment methods and tools |
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NEW QUESTION # 74
The seven Vs of Big Data are:
Answer: A
Explanation:
Within the DMBOK2 framing used by this certification material, the expanded characteristics are Volume, Velocity, Variety/Variability, Viscosity, Volatility, and Veracity. Option E is therefore the only choice that correctly contains the DAMA terms represented in the question. DMBOK-oriented study material explains that the original three Vs-Volume, Velocity, and Variety-were expanded to include Variability, Viscosity, Volatility, and Veracity in the broader characterization.
Volume concerns data quantity; Velocity concerns the rate of creation and processing; Variety/Variability concerns differing structures and changing representations; Viscosity describes difficulty in using or integrating the data; Volatility concerns how quickly usefulness or meaning changes; and Veracity addresses credibility and trustworthiness.
These characteristics have direct Data Quality implications. Increased variety creates semantic and structural consistency challenges. Velocity reduces the time available for traditional validation. Volatility affects currency and timeliness. Veracity is directly concerned with reliability.
DAMA's key point is that Big Data does not reduce the need for management discipline. Its scale and complexity increase the need for metadata, governance, automated profiling, lineage, and statistically driven quality controls.
Reference Topics: DAMA-DMBOK2 Big Data and Data Science - Big Data Characteristics; Volume; Velocity; Variety/Variability; Viscosity; Volatility; Veracity; Chapter 13 - Scalable Data Quality.
NEW QUESTION # 75
Profiling shows that whenever Account_Status is "Closed", Closure_Date is normally populated. However,
4% of closed accounts have a null Closure_Date. What type of profiling has revealed this issue?
Answer: A
Explanation:
This is cross-column dependency profiling. The issue cannot be identified merely by counting null values in Closure_Date because some nulls may be legitimate for active accounts. The defect becomes meaningful only when the relationship between two attributes is examined.
The implied business rule is: if Account_Status = Closed, then Closure_Date must be populated. Dependency profiling examines relationships among values and can reveal conditional completeness, inconsistent combinations, functional dependencies, and other multi-attribute patterns.
The next step is to confirm the inferred pattern with business stakeholders rather than automatically converting it into an enforced rule. There may be exceptional account categories where a closed status legitimately lacks a closure date.
Once approved, the rule should be documented as metadata, assigned to a Data Steward, measured against an agreed threshold, and monitored. Root-cause investigation can then determine whether missing dates arise from source-screen design, process bypass, conversion defects, or integration failures.
DAMA's revised Chapter 13 places Data Quality techniques in a more practical sequence and strengthens their linkage with Data Modeling and Metadata Management.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Profiling; Dependency Analysis; Completeness; Business Rules; Metadata Management.
NEW QUESTION # 76
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 # 77
The implementation of a 'Super Type - Sub Type' structure can use the following 2 options:
Answer: C
Explanation:
DAMA-DMBOK2 identifies two recognized approaches for resolving logical supertype-subtype abstractions when moving into physical database design: Subtype Absorption and Supertype Partition.
With Subtype Absorption, attributes belonging to subtype entities are incorporated into the table representing the supertype. Attributes that apply only to particular subtypes may consequently be nullable. This approach reduces the number of physical tables but can introduce sparsity and requires explicit rules to ensure subtype- specific attributes remain semantically valid.
With Supertype Partition, the attributes belonging to the supertype are carried into separate physical tables representing each subtype. This can simplify subtype-specific processing but introduces duplication of common structures and requires disciplined metadata and modelling control.
The choice has direct consequences for Data Quality. Subtype absorption requires validity and completeness rules to distinguish legitimate nulls from missing data. Supertype partition requires consistency controls to ensure shared attributes retain identical definitions and constraints across subtype tables. Metadata repositories should document subtype discriminators, attribute definitions, constraints, and inheritance rules.
DMBOK2 therefore treats the transformation as a deliberate physical modelling decision rather than the generic "merge/split" terminology presented in the distractors.
Reference Topics: DAMA-DMBOK2 Chapter 5 - Physical Data Modeling; Resolve Logical Abstractions; Chapter 13 - Validity, Completeness and Consistency; Metadata Management.
NEW QUESTION # 78
A goal of reference and master data management is for data to ensure shared data is:
Answer: B
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 # 79
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