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| Certification Vendor: | DAMA International |
|---|---|
| Exam Name: | Data Quality |
| Exam Number: | DQ-1220 |
| Exam Duration: | 90 minutes |
| Certificate Validity Period: | 3 years |
| Exam Format: | Scenario-Based, Multiple Choice |
| Exam Price: | $311 USD (member), $411 USD (non-member) |
| Passing Score: | 70% |
| Real Exam Qty: | 70-100 |
| Related Certifications: | CDMP Master DMF-1220 Data Management Fundamentals CDMP Associate CDMP Fellow DG-1220 Data Governance |
| Available Languages: | English |
| Recommended Training: | DAMA DMBOK Guide Official CDMP Study Resources |
| Exam Registration: | Testing Partner: Kryterion DAMA Official Registration |
| Sample Questions: | DAMA DQ-1220 Sample Questions |
| Exam Way: | In-person at authorized test centers or remote online proctored |
| Pre Condition: | Recommended: DMF-1220 (Data Management Fundamentals) or equivalent knowledge; 2+ years of data management experience |
| Official Syllabus URL: | https://dama.org/certification/cdmp/ |
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DAMA DQ-1220 exam covers a wide range of topics related to data quality management, including data profiling, data cleansing, data validation, and data standardization. DQ-1220 exam also covers key concepts such as data governance, data privacy, and data security. DQ-1220 exam is designed to test the candidate's ability to apply these concepts to real-world scenarios, making it an essential certification for professionals in the data management field.
DAMA DQ-1220 Certification Exam is a professional certification that focuses on data quality. Data Quality certification is designed to help individuals develop the knowledge and skills needed to effectively manage data quality in a variety of settings. DQ-1220 exam covers a wide range of topics, including data quality dimensions, data profiling techniques, and data quality assessment methodologies.
NEW QUESTION # 90
A dataset contains every required customer record, but 8% of the telephone numbers belong to different people. Which statement is correct?
Answer: A
Explanation:
The dataset can be complete while remaining inaccurate. Completeness measures whether the required records and values are present; Accuracy measures whether those values correctly represent reality.
If every customer has a telephone number, the field may achieve 100% completeness. However, if 8% of those numbers belong to other people, the values are inaccurate.
This distinction is one of the most important principles in Data Quality measurement. A populated field is not automatically trustworthy. Similarly, a syntactically valid value is not necessarily accurate.
DAMA-aligned guidance explicitly distinguishes these dimensions and notes that a complete dataset may still contain incorrect values.
The appropriate quality scorecard should therefore present completeness and accuracy separately rather than treating one as evidence of the other.
For high-risk customer contact processes, the business may define independent thresholds for both dimensions. Verification services, customer confirmation, authoritative sources, and exception handling may be needed to improve accuracy after basic completeness has already been achieved.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Completeness; Accuracy; Data Quality Dimensions; Metrics; Fitness for Purpose.
NEW QUESTION # 91
A lineage data tool provides:
Answer: C
Explanation:
A Data Lineage tool provides the capture and maintenance of source structures for each attribute on the data model. Lineage connects modeled data elements to their originating structures and records how those elements move and transform through downstream systems. DAMA-related practice material identifies this specific capability as the correct function of lineage tooling.
At attribute level, lineage may identify a source table and column, transformation logic, staging structures, intermediate mappings, target columns, and ultimately the report or analytical object in which the value appears. This information allows organizations to answer fundamental questions such as: Where did this value originate? What transformations were applied? Which systems depend on this field? What downstream assets will be affected if its definition changes?
For Data Quality, lineage is a critical diagnostic capability. If a value becomes inaccurate after a transformation, lineage helps isolate the point of failure and distinguish source-system defects from integration or reporting defects. It also supports impact analysis before quality-rule changes are deployed.
Metadata Management normally provides the repository and technical structures that preserve lineage.
Governance establishes ownership and documentation standards, while Data Stewards use lineage to understand business impact and coordinate remediation.
The other choices do not describe recognized lineage functions.
Reference Topics: DAMA-DMBOK2 - Metadata Management; Data Lineage; Data Architecture; Source- to-Target Mapping; Chapter 13 - Issue Investigation and Root-Cause Analysis.
NEW QUESTION # 92
Adoption of a Data Governance program is most likely to succeed:
Answer: E
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 # 93
The primary reason for identifying Critical Data Elements is to:
Answer: A
Explanation:
Critical Data Elements are identified so that organizations can concentrate governance and Data Quality effort where failure would create the greatest business impact.
Not every attribute deserves the same level of profiling, monitoring, stewardship, lineage documentation, control, and remediation. Applying identical controls to every field would usually be economically inefficient and may divert resources away from information that supports regulation, financial reporting, key operations, customer outcomes, or strategic decisions.
The DMBOK2 maintenance revision explicitly adds and clarifies the concept of a Critical Data Element within Chapter 13.
Once a CDE is identified, the organization can define applicable quality dimensions, measurable rules, thresholds, ownership, authoritative sources, lineage, issue escalation, and monitoring frequency.
Criticality is contextual. An attribute may be critical to one process and relatively insignificant to another. The decision should therefore reflect documented business use and risk rather than technical prominence.
Metadata remains essential because it links the CDE to definitions, systems, lineage, owners, and quality controls.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Critical Data Elements; Prioritization; Business Impact; Risk; Data Governance; Metadata.
NEW QUESTION # 94
Critical to the incremental development of the data warehouse is:
Answer: C
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 # 95
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