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The DAMA DQ-1220 exam is designed to test the knowledge and skills of professionals who are responsible for managing data quality. DQ-1220 exam covers a range of topics, including data quality assessment, data profiling, data cleansing, and data governance. DQ-1220 Exam is divided into multiple-choice questions, and candidates have 90 minutes to complete it.
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The purpose of the DAMA DQ-1220 Certification Exam is to provide professionals with the knowledge and skills necessary to effectively manage and maintain data quality within their organizations. DQ-1220 exam covers a wide range of topics, including data quality concepts, data profiling techniques, data cleansing practices, and data governance principles. By passing the certification exam, professionals can demonstrate their expertise in these areas and increase their credibility as data quality experts.
NEW QUESTION # 24
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: C
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 # 25
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: C
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 # 26
The implementation of a 'Super Type - Sub Type' structure can use the following 2 options:
Answer: A
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 # 27
Big data management requires:
Answer: E
Explanation:
DAMA-DMBOK2 establishes the guiding principle that Big Data management requires more discipline than relational data management. The reason is not simply data volume. Big Data environments combine large volumes with substantial variation in structure, source, velocity, semantics, and reliability. These characteristics increase the probability of uncontrolled duplication, inconsistent interpretation, poor provenance, inappropriate use, and undetected quality defects. DAMA's Big Data guidance specifically emphasizes disciplined management of metadata describing Big Data sources, their origins, and their value.
From a Data Quality perspective, scale does not make data fit for purpose. Large datasets still require defined quality expectations, profiling, monitoring, lineage, controlled transformations, ownership, and governance.
Metadata becomes particularly important because analysts must understand where datasets originated, how they were transformed, their meaning, and whether they are appropriate for a particular analytical objective.
Data Governance must consequently establish accountability, acceptable-use policies, security requirements, quality expectations, and escalation mechanisms. Statistical and automated controls are also essential because manual inspection becomes impractical at Big Data scale.
Neither larger budgets nor data-science certification substitutes for sound data-management discipline.
Likewise, reducing controls because data is large creates greater-not lower-operational and analytical risk.
Reference Topics: DAMA-DMBOK2 - Big Data and Data Science; Big Data Management Principles; Metadata Management; Data Governance; Chapter 13 - Fitness for Purpose and Data Quality Controls.
NEW QUESTION # 28
Adoption of a Data Governance program is most likely to succeed:
Answer: B
Explanation:
DAMA-DMBOK2 recommends an incremental implementation strategy for Data Governance because governance introduces changes in accountability, decision rights, standards, processes, and organizational behaviour. Attempting an enterprise-wide implementation immediately creates unnecessary cultural and operational risk.
The implementation guidance is explicit that most governance rollout strategies are incremental, commonly beginning with a significant initiative such as Master Data Management or with a particular region, business unit, or domain. Enterprise-wide deployment as the initial step is described as uncommon.
Incremental implementation provides several control advantages. The organization can validate roles and decision mechanisms, establish stewardship practices, prove business value, refine policies, develop metrics, and correct weaknesses before expanding the governance framework. This aligns closely with Data Quality management, where organizations normally prioritize Critical Data Elements and high-value domains rather than attempting to measure and remediate every data element simultaneously.
Executive sponsorship is important, but a mandate alone does not produce sustainable adoption. Likewise, individual leadership charisma cannot substitute for operating processes, accountability, metrics, and change management. A short consultant-driven exercise also conflicts with DAMA's treatment of governance as an ongoing organizational capability.
Incremental rollout therefore offers the strongest mechanism for embedding governance permanently while demonstrating measurable value.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Implementation Guidelines; Organization and Culture; Adjustment and Communication; Chapter 13 - Data Quality Improvement and Governance.
NEW QUESTION # 29
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