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| Certification Vendor: | DAMA International |
|---|---|
| Exam Name: | Data Quality |
| Exam Number: | DQ-1220 |
| Exam Format: | Multiple Choice |
| Passing Score: | 70% |
| Exam Duration: | 60 minutes |
| Real Exam Qty: | 80 |
| Available Languages: | English |
| Related Certifications: | CDMP - Certified Data Management Professional |
| Exam Price: | USD $400 (Full CDMP exam) |
| Certificate Validity Period: | No expiration for CDMP certificate (requires continuing education/renewal credits) |
| Sample Questions: | DAMA DQ-1220 Sample Questions |
| Exam Way: | Online proctored or Onsite testing center |
| Pre Condition: | No formal prerequisites; recommended experience in data management |
| Official Syllabus URL: | https://dama.org/certification/ |
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DAMA DQ-1220 exam is designed for professionals working in areas such as data governance, data management, data architecture, data quality, and analytics. DQ-1220 exam evaluates the competency of these professionals in ensuring high-quality data is available for the organization’s activities. DQ-1220 examination is run by the Data Management Association International.
DAMA DQ-1220 Certification Exam is a rigorous and comprehensive exam that tests a candidate’s understanding of data quality management concepts and practices. DQ-1220 Exam consists of multiple-choice questions and covers a variety of topics such as data profiling, data cleansing, data governance, data quality measurement, and data quality improvement. DQ-1220 exam is designed to evaluate a candidate’s ability to apply their knowledge in real-world scenarios and to solve problems related to data quality management.
NEW QUESTION # 112
An address-validation service reports that 96% of customer addresses conform to an approved postal format.
What does this metric demonstrate most directly?
Answer: B
Explanation:
The metric demonstrates Validity because it measures whether address values conform to a defined format or standard.
Validity checks whether data satisfies specified rules concerning datatype, pattern, permitted values, ranges, or structures. Postal formatting rules might require approved postcode structure, valid country-code syntax, or required address components.
The metric does not prove that the address belongs to the correct customer. A perfectly formatted address may still be factually wrong. That would be an Accuracy problem.
DAMA-aligned government guidance defines validity in terms of conformity to expected format, type, range, and other predefined rules.
A mature address-quality program may combine validity with verification against authoritative postal reference data, duplication checks, completeness rules, and currency monitoring.
Quality scorecards should therefore label the metric precisely rather than calling the result a generic "accuracy score." Reference Topics: DAMA-DMBOK2 Chapter 13 - Validity; Accuracy; Data Quality Metrics; Address Quality; Reference Data.
NEW QUESTION # 113
A Data Quality process has remained within statistical control limits for six months, but management wants the average defect rate reduced further. What is the most appropriate conclusion?
Answer: D
Explanation:
A process can be statistically stable yet still perform at an unacceptable quality level. Statistical control means that variation is predictable within the established process; it does not mean that the average level of defects satisfies business expectations.
For example, a process may consistently produce a 3% defect rate with very little month-to-month variation.
If the business requirement is below 0.5%, the process is stable but incapable of meeting the desired quality level without improvement.
The appropriate response is therefore deliberate process improvement aimed at changing the underlying process and reducing its baseline defect rate. This differs from reacting to random individual observations within normal control limits.
After improvement, new performance data should be collected and control parameters reevaluated once the revised process reaches a stable state.
DAMA's revised Chapter 13 explicitly maps Shewhart and Deming improvement-cycle stages to Data Quality processes and preserves Statistical Process Control as supporting material.
The central principle is that control and capability are different questions: control asks whether the process is stable; business quality requirements determine whether that stable performance is good enough.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Statistical Process Control; Process Stability; Continuous Improvement; Shewhart/Deming Cycle; Thresholds.
NEW QUESTION # 114
Which of the following is the best example of a 'documented data quality rule'?
Answer: D
Explanation:
Option D is the strongest example because it expresses a Data Quality requirement that is specific, measurable, associated with identified data, and explicitly connected to business need. It identifies the relevant data-transaction data from satellite systems-sets a measurable timeliness threshold of 12:05 a.m., and explains the business reason: ensuring that branches have access to actual customer balances for the required operating window. Published versions of this DAMA question identify the same choice.
This is fundamentally a Timeliness rule. DMBOK2 emphasizes that Data Quality requirements should be defined according to business expectations rather than vague aspirations. A useful rule needs sufficient precision to support objective measurement, exception reporting, and remediation.
Option E captures an important quality expectation-actual customer balances-but it lacks the same precise operational threshold. Option A is a security/confidentiality requirement rather than a Data Quality rule.
Options B and C primarily describe processing procedures and do not express the business-quality requirement as cleanly.
Once documented, the rule should be managed as metadata, assigned to an accountable steward, linked to its Critical Data Elements, monitored through metrics, and escalated when the specified threshold is breached.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Quality Requirements; Data Quality Rules; Timeliness; Measurement; Business Impact; Metadata Management.
NEW QUESTION # 115
An MDM hub receives three customer records that have been confirmed to represent the same person. Before creating the mastered representation, what process determines which source values should populate each attribute?
Answer: A
Explanation:
The process is Survivorship. After records have been matched and determined to represent the same entity, survivorship rules determine which attribute values should populate the mastered or preferred representation.
For example, the organization may select the most recently verified telephone number, prioritize a government-verified legal name, or prefer addresses originating from a designated Customer Relationship Management system. Different attributes can use different survivorship rules.
Survivorship is not simply "choose the newest value." The appropriate method may consider source trust, verification status, recency, completeness, confidence scores, stewardship decisions, or combinations of these factors.
The rules must be governed because they determine what the organization subsequently treats as trusted master data. Metadata should record the rule, selected source, lineage, and effective date so the mastered record remains explainable.
DAMA identifies Reference and Master Data Management as responsible for maintaining consistency in core entities such as customers, products, and locations and for supporting trusted shared information.
The Data Quality dimensions involved commonly include Accuracy, Consistency, Currency, Completeness, and Uniqueness.
Reference Topics: DAMA-DMBOK2 Chapter 10 - Master Data Management; Matching; Survivorship; Golden Record; Chapter 13 - Consistency and Accuracy.
NEW QUESTION # 116
How is Data Governance defined?
Answer: C
Explanation:
DAMA-DMBOK2 defines Data Governance as the exercise of authority and control over the management of data assets. This definition distinguishes governance from the operational execution of Data Management.
Independent literature describing DAMA's framework uses the same core formulation, and the DAMA Wheel places Data Governance across the other knowledge areas through this authority-and-control function.
Governance establishes decision rights, accountability, policies, standards, stewardship, issue escalation, compliance expectations, and mechanisms for monitoring whether data is being managed appropriately. It determines who is authorized to make decisions and what rules must be followed; individual Data Management knowledge areas then execute the corresponding operational activities.
Option E is closer to the broad definition of Data Management, because it refers to planning, implementation, and control across the information lifecycle. Option A describes an assessment or maturity activity. Option B describes selected governance benefits rather than its definition, while option C characterizes a collection of functions.
For Data Quality, governance provides the authority needed to define critical data, approve quality requirements and thresholds, assign accountability, prioritize remediation, and enforce standards. Without this authority structure, profiling may identify defects but the organization may lack the decision mechanisms required to correct their underlying causes.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Data Governance Definition; Authority and Control; Policies and Standards; Chapter 13 - Data Quality Governance.
NEW QUESTION # 117
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