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| Section | Weight | Objectives |
|---|---|---|
| Data Quality Strategy and Governance | 20% | - Roles and responsibilities - Data quality strategy and policy - Integration with data governance |
| Data Quality Concepts and Principles | 15% | - Definition and importance of data quality - Data quality dimensions - Data quality in DMBOK framework |
| Data Quality Assessment and Profiling | 25% | - Data profiling techniques - Defining quality rules and metrics - Assessment methods and tools |
| Tools, Techniques and Implementation | 20% | - Data quality in lifecycle management - Data quality tools and technologies - Business value and ROI |
| Data Quality Improvement | 20% | - Data cleansing and standardization - Root cause analysis - Monitoring and continuous improvement |
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NEW QUESTION # 59
Database monitoring tools measure key database metrics, such as:
Answer: E
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 # 60
The DMBoK 'Environmental Factors hexagon' shows the relationship between:
Answer: A
Explanation:
The DAMA-DMBOK2 Environmental Factors Hexagon illustrates the relationship between People, Process, and Technology. It provides a visual key for interpreting the Knowledge Area Context Diagrams and emphasizes that effective Data Management depends on the coordinated interaction of these three dimensions.
DMBOK2 places Goals and Principles at the center of the hexagon because they guide how people perform activities and how technology is selected and used. People-related factors include roles, responsibilities, organization, and culture. Process-related factors include activities, techniques, and methods. Technology- related factors include tools and the mechanisms that support Data Management deliverables.
This model is especially relevant to Data Quality. A profiling or cleansing platform alone cannot establish sustainable quality. The organization also needs accountable Data Owners and Data Stewards, repeatable issue-management and improvement processes, business-defined rules, and governance mechanisms.
Conversely, well-designed processes will fail if supporting technologies cannot monitor or enforce required controls.
Option A relates more closely to the structure of DMBOK Knowledge Area Context Diagrams. The DAMA Wheel represents the Knowledge Areas, while the Environmental Factors Hexagon specifically emphasizes the People-Process-Technology relationship.
Reference Topics: DAMA-DMBOK2 Chapter 1 - DAMA-DMBOK Framework; Environmental Factors Hexagon; People, Process and Technology; Chapter 13 - Data Quality Organization, Processes and Tools.
NEW QUESTION # 61
A customer moved house three months ago. The CRM still contains the customer's former address, although the record was originally correct when created. Which Data Quality dimension best describes the current problem?
Answer: C
Explanation:
The issue is Currency. Currency concerns whether data remains sufficiently aligned with the current real- world state. The address was accurate when originally captured, but reality changed and the system was not updated.
This distinction matters because Data Quality can degrade over time without any technical error being introduced. Customer addresses, employment details, product prices, organizational structures, regulatory classifications, and contact details all have varying rates of change. A dataset that was reliable last year may no longer be fit for operational use today.
DAMA's current Chapter 13 revision explicitly identifies Currency as one of the nine standard Data Quality dimensions. Related DAMA-derived guidance also notes that real-world information changes and can therefore become outdated even when it was initially correct.
Organizations should establish refresh expectations according to business use. A marketing mailing list may tolerate a different update interval from an emergency-contact database.
Metadata should document refresh frequency and source authority, while quality monitoring can identify records exceeding acceptable age thresholds.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Currency; Accuracy; Timeliness; Data Aging; Monitoring; Fitness for Purpose.
NEW QUESTION # 62
A data lake and a data warehouse are the same concepts in so far as:
Answer: E
Explanation:
Although Data Lakes and Data Warehouses have materially different architectures, both support the broader objective of making organizational data available for reporting, analysis, analytics, and decision support.
Therefore, option B identifies their legitimate conceptual overlap.
A traditional Data Warehouse generally contains integrated, curated, structured data designed for repeatable Business Intelligence, reporting, and analytical workloads. A Data Lake typically retains larger volumes of raw or less-structured information and supports flexible processing, exploration, Data Science, and advanced analytics. Both can therefore participate in analytical data pipelines even though their approaches to schema, transformation, governance, and consumption differ.
They do not inherently duplicate every item of operational data. Nor must a Data Lake produce dimensional or star-schema models; that modeling approach is more closely associated with traditional warehouse implementations. Governance is required for both rather than being the defining difference between them.
From a Data Quality standpoint, warehouses typically apply substantial cleansing and conformity before consumption. Lakes may retain raw values and defer interpretation, which increases the importance of metadata, provenance, cataloging, profiling, and consumer awareness.
Reference Topics: DAMA-DMBOK2 - Data Warehousing and Business Intelligence; Big Data; Data Lakes; Analytical Data; Metadata; Chapter 13 - Data Preparation and Fitness for Purpose.
NEW QUESTION # 63
Profiling reveals that an Age field has a minimum value of -7 and a maximum value of 263. What should the Data Quality team do first?
Answer: A
Explanation:
The values should first be treated as potential exceptions requiring investigation. Profiling identifies anomalies; it does not automatically prove that every unusual value is incorrect or specify the appropriate remediation.
Negative age and 263 years are highly improbable for a conventional person-age attribute, suggesting a validity or reasonableness defect. However, the Data Quality team should confirm the business definition, unit of measure, derivation logic, source mappings, special-value conventions, and intended population before changing the records.
Blindly replacing values with an average introduces fabricated data and destroys traceability. Deleting the records may also remove legitimate information elsewhere in the record. Increasing the datatype range addresses technical storage rather than semantic correctness.
A disciplined Data Quality process moves from profiling to rule validation, issue assessment, root-cause analysis, remediation, and monitoring. The business rule may ultimately specify an acceptable age range, but that rule should be governed and documented rather than inferred from individual anomalies.
Metadata is essential because it should describe whether Age is stored directly, calculated from Date of Birth, or encoded using another convention.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Profiling; Validity; Reasonableness; Root-Cause Analysis; Data Quality Rules; Metadata.
NEW QUESTION # 64
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