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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Quality Strategy and Governance | 20% | - Roles and responsibilities - Integration with data governance - Data quality strategy and policy |
| Topic 2: Data Quality Improvement | 20% | - Data cleansing and standardization - Root cause analysis - Monitoring and continuous improvement |
| Topic 3: Data Quality Assessment and Profiling | 25% | - Defining quality rules and metrics - Data profiling techniques - Assessment methods and tools |
| Topic 4: Tools, Techniques and Implementation | 20% | - Business value and ROI - Data quality in lifecycle management - Data quality tools and technologies |
| Topic 5: Data Quality Concepts and Principles | 15% | - Definition and importance of data quality - Data quality dimensions - Data quality in DMBOK framework |
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NEW QUESTION # 33
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?
Answer: C
Explanation:
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.
NEW QUESTION # 34
A form permits users to enter any text into a field that should contain an ISO country code. What is the strongest preventive Data Quality control?
Answer: C
Explanation:
The strongest preventive control is to restrict data entry to the approved country-code domain. Prevention at the point of creation is generally more effective and less costly than repeatedly correcting invalid values downstream.
A controlled drop-down list, validated API, or reference-domain lookup ensures that users can select only permitted codes. This directly supports Validity and improves Consistency across applications.
The rule should be linked to centrally governed Reference Data rather than hard-coded independently in each application. That allows changes to the permitted domain to be managed systematically.
Downstream cleansing remains useful for historical defects or external data, but it should not be the primary control when the organization controls the source-entry process.
DAMA's framework links Reference/Master Data Management to consistency of shared values and Chapter
13 to defining and monitoring Data Quality controls.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Preventive Controls; Validity; Data Creation; Chapter
10 - Reference Data; Controlled Domains.
NEW QUESTION # 35
What is a steward?
Answer: A
Explanation:
In DAMA terminology, stewardship is based on the established concept of managing an asset on behalf of another party. DAMA-DMBOK2 defines the underlying term directly: "A steward is a person whose job it is to manage the property of another person." It then applies that concept specifically to data: Data Stewards manage data assets on behalf of others and in the best interests of the organization.
A Data Steward is therefore much more than a stakeholder or sponsor. Stewardship carries explicit operational accountability. Typical responsibilities include defining and clarifying business data, participating in issue resolution, supporting standards and policies, establishing quality requirements, approving metadata definitions, and ensuring governance decisions are applied within the steward's data domain.
The relationship to Data Quality is particularly important. DMBOK2 identifies Data Stewards as participants in identifying and resolving data-related issues and in ensuring governance policies are followed. They frequently establish or approve Critical Data Elements, Data Quality rules, acceptable thresholds, business definitions, and remediation priorities. Metadata Management records those definitions and rules, while Master Data Management applies stewardship decisions to shared entities such as Customer, Product, Supplier, or Location.
A stakeholder may have an interest in the data, and a sponsor may provide authority or funding, but neither term inherently includes stewardship accountability.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Types of Data Stewards; Stewardship; Data Quality Issue Management; Chapter 13 - Data Quality Governance and Responsibilities.
NEW QUESTION # 36
The seven Vs of Big Data are:
Answer: D
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 # 37
A business case for adding a new master data management solution is dependent on achieving greater value from:
Answer: B
Explanation:
An MDM investment is justified when the business value obtained from centralized coordination of shared data exceeds the additional cost of managing and integrating another controlled representation of that data.
This is the economic trade-off expressed by option C and is also the intended answer in published versions of this DAMA question.
DAMA-DMBOK2 positions Master Data Management as a mechanism for improving consistency and control over enterprise entities such as customers, products, suppliers, employees, and locations. Uncoordinated local copies create redundant maintenance, inconsistent definitions, duplicate records, reconciliation costs, and integration complexity. DAMA sources emphasize that controlling shared master and reference data reduces both cost and risk generated by inconsistencies between systems.
However, introducing an MDM repository also incurs costs: integration, stewardship, matching, survivorship logic, synchronization, governance, maintenance, and potentially another physical copy of shared information.
The business case must therefore demonstrate that improved coordination, quality, reuse, reporting, and operational consistency outweigh those costs.
MDM does not require all contributing systems to be decommissioned, nor does it eliminate Reference Data Management. Similarly, "mining golden records" is not the fundamental economic basis for MDM.
Reference Topics: DAMA-DMBOK2 Chapter 10 - Master Data Management; Business Drivers; Shared Data; Central Coordination; Data Integration; Data Quality and Stewardship.
NEW QUESTION # 38
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