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| Section | Weight | Objectives |
|---|---|---|
| Data Quality Governance | 15% | - Policies and Procedures - Continuous Monitoring - Data Stewardship - Data Quality Roles and Responsibilities |
| Data Quality Fundamentals | 20% | - Definition of Data Quality - Data Quality in the Data Lifecycle - Dimensions of Data Quality (completeness, accuracy, consistency, timeliness, validity) |
| Data Quality Improvement | 20% | - Data Cleansing Techniques - Data Enrichment - Standardization - Validation Rules |
| Data Quality Frameworks and Standards | 20% | - DAMA DMBOK Framework - ISO 8000 (Data Quality) - Other Industry Standards |
| Data Quality Assessment and Measurement | 25% | - Benchmarking - Metrics and KPIs - Profiling Techniques - Root Cause Analysis |
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NEW QUESTION # 19
Achieving security risk reduction in an organisation begins with developing what?
Answer: E
Explanation:
Effective security risk reduction begins by understanding what data exists, how sensitive or critical it is, and where it resides. A classification model provides this foundation by classifying data concepts according to protection requirements and linking those concepts to their physical implementations. The certification material reflects this DAMA principle directly.
Data cannot be protected proportionately if the organization does not know whether it represents public information, internal operational information, personally identifiable information, financial data, intellectual property, regulated information, or another sensitive category. Classification allows the organization to apply appropriate controls according to risk rather than treating every data asset identically.
Once classification is established, security teams can define access requirements, encryption needs, monitoring, retention controls, masking, authorization policies, and other safeguards. Metadata is essential because classifications must ultimately be connected to actual databases, files, attributes, interfaces, and repositories.
A firewall alone does not provide data-level protection, and role classification by itself addresses only one dimension of security. Similarly, an Enterprise Data Model describes organizational data structures but does not replace security classification.
Reference Topics: DAMA-DMBOK2 Chapter 7 - Data Security; Data Classification; Risk Reduction; Sensitive Data Discovery; Metadata Management; Chapter 13 - Integrity and Controlled Use.
NEW QUESTION # 20
A search engine database is populated by a web crawler or spider software that usually processes as:
Answer: C
NEW QUESTION # 21
Critical to the incremental development of the data warehouse is:
Answer: E
Explanation:
A strong Release Management process is critical when a Data Warehouse evolves incrementally. DAMA- DMBOK2 states directly that Release Management supports incremental development by coordinating new capabilities, enhancements, production deployment, and recurring maintenance of deployed warehouse assets.
A Data Warehouse is rarely completed in one implementation. Business requirements evolve, additional subject areas are onboarded, models are extended, transformation rules change, new reports are introduced, and defects are corrected. Release Management provides the controlled mechanism for packaging these changes into predictable production increments.
This requires prioritization of the backlog, coordination between business and technical teams, regression testing, deployment control, documentation, reconciliation, and validation of new or changed data structures.
Without disciplined release management, incremental development can produce incompatible transformations, unstable reports, inconsistent historical treatment, and uncontrolled changes to definitions.
Data Quality should therefore form part of each release gate. New mappings and transformations should be profiled and reconciled; quality thresholds should be retested; metadata and lineage must be updated; and known exceptions should be documented.
Agile development may be used as a delivery method, but DAMA specifically identifies Release Management as the process critical to sustaining incremental warehouse evolution.
Reference Topics: DAMA-DMBOK2 Data Warehousing and Business Intelligence - Maintain Data Products; Release Management; Incremental Development; Chapter 13 - Quality Monitoring and Change Control.
NEW QUESTION # 22
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: D
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 # 23
A customer's date of birth is stored as 12/04/1980. The value conforms to the database datatype and accepted date format, but the customer's verified date of birth is 21/04/1980. Which Data Quality dimension is primarily violated?
Answer: D
Explanation:
The primary failure is Accuracy. Accuracy concerns whether a stored value correctly represents the real- world entity, event, or fact it is intended to describe. The recorded date is syntactically acceptable and conforms to the expected datatype and format, so it may satisfy Validity while still being factually wrong.
This distinction is fundamental in Data Quality assessment. A validity rule can determine whether a value is structurally permissible-for example, whether a date conforms to an approved pattern or whether a month lies between 1 and 12. It cannot by itself prove that the date belongs to the correct person. Government guidance based on DAMA dimensions makes the same distinction: a value can be valid while remaining inaccurate.
The strongest accuracy control would compare the value with an authoritative or independently verified source. Metadata should document the authoritative source and lineage, while governance should assign responsibility for resolving discrepancies.
The scenario therefore demonstrates why Data Quality programs must avoid assuming that format compliance equals correctness.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Accuracy; Validity; Authoritative Sources; Data Quality Rules; Metadata Lineage.
NEW QUESTION # 24
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