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| Certification Vendor: | DAMA International |
|---|---|
| Exam Name: | Data Quality |
| Exam Number: | DQ-1220 |
| Exam Format: | Scenario-Based, Multiple Choice |
| Passing Score: | 70% |
| Available Languages: | English |
| Exam Price: | $311 USD (member), $411 USD (non-member) |
| Related Certifications: | CDMP Fellow DG-1220 Data Governance CDMP Associate DMF-1220 Data Management Fundamentals CDMP Master |
| Certificate Validity Period: | 3 years |
| Real Exam Qty: | 70-100 |
| Exam Duration: | 90 minutes |
| Recommended Training: | DAMA DMBOK Guide Official CDMP Study Resources |
| Exam Registration: | DAMA Official Registration Testing Partner: Kryterion |
| 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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The DAMA DQ-1220 Exam consists of 100 multiple-choice questions and you will be given 2 hours to complete it. The questions are designed to test your knowledge of data quality concepts, methodologies, and tools. You will need to have a good understanding of data profiling techniques, data quality metrics, data validation rules, data standardization methods, and data cleansing techniques.
NEW QUESTION # 104
A dataset contains every required customer record, but 8% of the telephone numbers belong to different people. Which statement is correct?
Answer: C
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 # 105
Following the rollout of a data issue process, there have been no issues recorded in the first month. The reason for this might be:
Answer: B
Explanation:
A complete absence of reported issues immediately after introducing an enterprise Data Issue process is more likely to indicate lack of confidence in the governance process than genuinely flawless enterprise data. The certification material explicitly identifies lack of credibility in the process's ability to effect change as the plausible explanation.
Effective issue management depends on organizational trust. Employees need to believe that documenting an issue will lead to triage, ownership, escalation, root-cause investigation, remediation, and appropriate communication. If previous problems disappeared into a queue without action-or if raising defects creates organizational friction-users may simply stop reporting them.
DAMA-DMBOK2 treats Data Governance implementation as an organizational-change challenge rather than a purely procedural exercise. Governance must demonstrate authority, responsiveness, transparency, and measurable outcomes to establish credibility.
For Data Quality, issue volumes must also be interpreted carefully. "Zero issues reported" is not equivalent to
"zero defects." Complementary evidence should come from profiling, automated monitoring, quality metrics, user feedback, reconciliation, and operational outcomes.
Management should investigate reporting barriers, communicate resolved cases, establish clear escalation paths, and demonstrate that identified problems produce tangible improvements.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Governance Adoption and Credibility; Data Issue Management; Chapter 13 - Issue Identification, Escalation, Root-Cause Analysis and Remediation.
NEW QUESTION # 106
A form permits users to enter any text into a field that should contain an ISO country code. What is the strongest preventive Data Quality control?
Answer: D
Explanation:
The strongest preventive control is to restrict data entry to the approved country-code domain. Prevention at the point of creation is generally more effective and less costly than repeatedly correcting invalid values downstream.
A controlled drop-down list, validated API, or reference-domain lookup ensures that users can select only permitted codes. This directly supports Validity and improves Consistency across applications.
The rule should be linked to centrally governed Reference Data rather than hard-coded independently in each application. That allows changes to the permitted domain to be managed systematically.
Downstream cleansing remains useful for historical defects or external data, but it should not be the primary control when the organization controls the source-entry process.
DAMA's framework links Reference/Master Data Management to consistency of shared values and Chapter
13 to defining and monitoring Data Quality controls.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Preventive Controls; Validity; Data Creation; Chapter
10 - Reference Data; Controlled Domains.
NEW QUESTION # 107
The need to manage data movement efficiently is a primary driver for:
Answer: B
Explanation:
The need to manage data movement efficiently is a primary business driver for Data Integration and Interoperability (DII). DAMA-DMBOK2 states this directly. Modern organizations operate hundreds or thousands of databases, applications, files, services, data stores, external interfaces, and analytical platforms.
Data must continually move among these environments, often across organizational boundaries.
Without disciplined integration management, data movement becomes fragmented, expensive, difficult to monitor, and highly dependent on duplicated point-to-point interfaces. DII addresses this problem by establishing controlled mechanisms for extraction, transformation, messaging, replication, orchestration, APIs, data virtualization, and other forms of information exchange.
From a Data Quality perspective, every movement of data presents an opportunity either to preserve quality or to damage it. Source-to-target mappings must maintain semantic meaning, transformations must be controlled, reference values must remain consistent, and lineage must show how values were altered.
Metadata Management therefore records mappings, interface definitions, transformation rules, and lineage.
Master Data Management uses integration mechanisms to distribute governed master and reference data consistently between systems.
Data Warehousing is a major consumer of integration capabilities, but the broader discipline whose explicit driver is efficient movement across systems is Data Integration and Interoperability.
Reference Topics: DAMA-DMBOK2 Chapter 8 - Business Drivers; Data Integration and Interoperability; Data Movement; Chapter 13 - Consistency, Integrity and Transformation Quality.
NEW QUESTION # 108
What is the final step in the development of a business-data-driven roadmap?
Answer: C
Explanation:
A business-data-driven roadmap translates the desired enterprise data architecture into an executable sequence of initiatives. DAMA-DMBOK2 explains that business capabilities consume and produce data, which creates dependencies between capabilities. The roadmap begins with relatively independent capabilities and progresses toward capabilities whose execution depends on data generated elsewhere. The architecture therefore establishes a coherent dependency chain before implementation work is organized.
The decisive final planning action is to turn that dependency sequence into implementable projects.
Consequently, establishing the sequence of projects that will establish the data value chain is the best answer.
Describing the data value chain, analysing data flows, identifying dependency challenges, and resequencing capabilities are analytical steps that precede portfolio sequencing.
DMBOK2 states that the roadmap should move from capabilities with the least dependency toward those with progressively greater dependencies, thereby providing the implementation order. It also emphasizes integrating the Enterprise Data Architecture roadmap with enterprise milestones, resources, and business- capability workstreams.
From a Data Quality perspective, sequencing matters because upstream master and reference data must reach an acceptable quality state before downstream transactional processes can depend on it.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Data Architecture; Develop a Roadmap; Data Dependencies; Data Value Chain; Chapter 13 - Data Quality and other Knowledge Areas.
NEW QUESTION # 109
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