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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Quality Concepts and Principles | 15% | - Data quality in DMBOK framework - Data quality dimensions - Definition and importance of data quality |
| Topic 2: Data Quality Improvement | 20% | - Monitoring and continuous improvement - Root cause analysis - Data cleansing and standardization |
| Topic 3: Data Quality Assessment and Profiling | 25% | - Assessment methods and tools - Data profiling techniques - Defining quality rules and metrics |
| Topic 4: Tools, Techniques and Implementation | 20% | - Data quality tools and technologies - Business value and ROI - Data quality in lifecycle management |
| Topic 5: Data Quality Strategy and Governance | 20% | - Data quality strategy and policy - Integration with data governance - Roles and responsibilities |
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NEW QUESTION # 112
The goal of data governance is to enable an organisation to manage data as an asset. To achieve this, the DG programs must be:
Answer: A
Explanation:
DAMA-DMBOK2 explicitly states that a Data Governance program must be sustainable. Governance is not a temporary implementation project that ends after policies, committees, or stewardship roles are established. It is an ongoing organizational capability requiring continuing leadership, sponsorship, ownership, decision rights, and operational integration.
The DMBOK2 governance guidance describes sustainable governance as "sticky": it must survive beyond its initial implementation and become embedded in normal business and Data Management practices.
Sustainable governance depends specifically on business leadership, sponsorship, and ownership.
This distinction matters for Data Quality because quality improvement is likewise continuous. New applications, data sources, business processes, regulatory requirements, and transformations continually create new risks. Governance must therefore continue assigning accountability, approving definitions and quality rules, resolving cross-domain disputes, and overseeing remediation.
Options focused on financial registration or assigning a dollar value confuse data-as-an-asset thinking with formal accounting treatment. Data valuation can support investment decisions, but it is not a prerequisite for Data Governance. Likewise, treating governance as a fixed-duration initiative contradicts the operating model described by DAMA.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Data Governance Goals and Principles; Sustainable Governance; Leadership; Sponsorship; Ownership; Chapter 13 - Data Quality Governance.
NEW QUESTION # 113
What is Data Stewardship?
Answer: A
Explanation:
DAMA-DMBOK2 defines Data Stewardship in terms of accountability and responsibility for data and for the processes that ensure its effective control and use. The wording in option E closely matches the DMBOK2 definition: stewardship describes accountability and responsibility for data and processes that ensure the effective control and use of data assets.
Stewardship may be formalized through named positions or responsibilities embedded within existing business roles. Its practical scope commonly includes managing business terminology, defining valid values and business rules, establishing or approving Data Quality requirements, resolving data issues, applying standards, and supporting governance decisions.
Option C is too narrow and also confuses stewardship with the broader policy-setting responsibilities of Data Governance. Stewards participate in developing and implementing policies and standards, but stewardship is not merely a policy-creation role. Similarly, it is not a privacy technology function or a project-management construct.
Within Data Quality Management, Data Stewards are critical because quality must be defined relative to business requirements. They help determine what "fit for purpose" means, establish acceptable thresholds, prioritize defects according to business impact, and participate in root-cause remediation.
Metadata Management records stewardship decisions, while Master Data Management uses those decisions to govern shared enterprise entities.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Data Stewardship; Steward Responsibilities; Business Glossary; Chapter 13 - Data Quality Governance and Issue Management.
NEW QUESTION # 114
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 # 115
A Data Quality team wants to understand whether defect rates are improving month by month rather than viewing only the latest percentage. Which approach is most useful?
Answer: A
Explanation:
Trend analysis is most useful because it shows how a quality metric changes over time rather than presenting a single isolated measurement.
A current defect rate of 4% has different implications depending on whether the previous months were 12%,
8%, and 6%, or 1%, 2%, and 3%. The first pattern indicates improvement; the second indicates deterioration.
Trend analysis therefore provides context for management decisions, helps determine whether remediation is producing sustained results, and can identify emerging degradation before thresholds are breached.
For stable, regularly measured processes, statistical control charts can add further value by distinguishing routine variation from signals that warrant investigation.
Metrics should be measured consistently over time. Changing definitions, populations, or calculation methods without documenting them can make apparent trends misleading.
Metadata should preserve metric definitions, threshold changes, and measurement logic to ensure comparability.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Monitoring; Trend Analysis; Statistical Control; Metrics; Continuous Improvement.
NEW QUESTION # 116
Data Staging areas are populated from source databases using extract functions, transformed and then:
Answer: C
Explanation:
The conventional flow is Extract, Transform, and Load (ETL). Data is extracted from source systems, placed in a staging environment where required transformations are performed, and then loaded into the target Data Warehouse. Consequently, the transformed staging data is made ready and loaded into the Data Warehouse.
DAMA-oriented training material presents this exact progression.
The staging area provides a controlled environment in which source data can be integrated without imposing transformation workloads directly on operational systems. Typical activities include datatype conversion, standardization, code translation, matching, deduplication, validation, derivation, consolidation, and handling rejected records.
From a Data Quality perspective, staging is especially important because many quality controls can be applied before data reaches the analytical repository. Profiling can identify unexpected distributions, invalid values, missing records, duplicate entities, referential-integrity defects, and inconsistent reference values. Failed records can then be quarantined or remediated according to governed rules.
Although metrics and profiling may occur in staging, they are supporting activities rather than the final destination in the ETL sequence. Likewise, metadata repositories document the transformation rather than serving as the main target for the transformed business data.
Reference Topics: DAMA-DMBOK2 - Data Warehousing and Business Intelligence; ETL; Staging Areas; Data Integration; Chapter 13 - Profiling, Cleansing, Standardization and Validation.
NEW QUESTION # 117
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