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Passing the DAMA DQ-1220 Exam demonstrates that an individual has a broad understanding of the principles of data quality and the ability to apply them in real-world situations. It is an internationally recognized certification that can help professionals advance their careers and enhance their credibility in the field of data management.
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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.
DAMA DQ-1220 (Data Quality) Exam is an internationally recognized certification that assesses a professional's knowledge and skills in the field of data quality management. DQ-1220 Exam is designed to test an individual's ability to evaluate, measure, and improve the quality of data within an organization. DQ-1220 exam is offered by the Data Management Association (DAMA) International, which is a not-for-profit, vendor-independent association of professionals in the field of data management.
NEW QUESTION # 97
During initial profiling of a Country_Code attribute, the analyst wants to identify unexpected values such as
"UKK", "ENG", and blanks. Which profiling output is most useful?
Answer: A
Explanation:
A frequency distribution is the most useful profiling output because it shows each distinct value and how often it occurs. For a controlled attribute such as Country_Code, this quickly reveals unexpected values, misspellings, blanks, obsolete codes, and disproportionately common defaults.
Data profiling is an analytical process used to examine actual data populations and determine their structure, content, patterns, distributions, and potential defects. Frequency analysis is particularly valuable for categorical attributes because it provides evidence about the real values present rather than relying only on declared metadata.
The result can be compared with an authoritative Reference Data domain. Values outside the approved code set become validity exceptions, while unusual frequencies may indicate mapping defects or default-value problems.
This is also where Metadata Management and Reference Data Management interact strongly with Data Quality. Metadata identifies the expected definition and domain; Reference Data provides the authorized codes; profiling determines whether operational values actually conform.
A data model can show that Country_Code exists and perhaps its datatype, but it does not reveal the actual value distribution currently stored in production.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Profiling; Frequency Analysis; Validity; Reference Data; Metadata Management.
NEW QUESTION # 98
A Data Quality rule states that every active supplier must have a valid tax identifier. Who should approve whether the rule is mandatory for the Supplier domain?
Answer: D
Explanation:
Whether the rule is mandatory is a business governance decision and should be approved through the appropriate Data Owner, Data Steward, or governance authority responsible for the Supplier domain.
A developer can implement a database constraint, but technical implementation does not establish business policy. The organization must determine whether every active supplier genuinely requires a tax identifier, whether exceptions exist by jurisdiction or supplier type, and what consequences should follow when the rule is violated.
The approved rule should be documented as metadata and linked to the relevant business term, physical fields, authoritative sources, threshold, and responsible steward.
This illustrates the separation between governance and execution. Governance defines and authorizes the requirement; operational and technical teams implement the validation and monitoring.
DAMA's public framework places Governance across the Data Management knowledge areas and Metadata Management as the mechanism for maintaining definitions, lineage, and usage context.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Governance and Stewardship; Chapter 13 - Data Quality Rules; Metadata Management; Supplier Master Data.
NEW QUESTION # 99
Which of the following is a directive that codifies principles and management intent into fundamental rules governing the creation, acquisition, integrity, security, quality, and use of data and information?
Answer: B
Explanation:
The definition describes Data Policies. DAMA-DMBOK2 states that data policies are directives that translate organizational principles and management intent into fundamental rules controlling how data is created, acquired, protected, maintained, assured for quality, and used.
The distinction between governance and policy is important. Data Governance is the broader authority and decision-making framework. Policies are one of the principal instruments produced or sponsored through that framework. They define what must or must not occur. Standards and procedures subsequently translate those policies into specific implementation requirements and operational methods.
For Data Quality, a policy might require business-critical data to have designated ownership, documented definitions, measurable quality rules, monitored thresholds, and formal remediation processes. The detailed thresholds themselves may reside in standards or rule repositories rather than in the high-level policy.
Metadata Management makes policy operational by documenting definitions, classifications, ownership, lineage and applicable quality rules. Master Data Management applies those policies to shared business entities and controlled reference values. Data Stewards then monitor compliance and escalate violations through governance mechanisms.
Data asset valuation is concerned with determining economic value, while Data Management encompasses the complete discipline. Neither provides the specific "directive" definition stated in the question.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Data Policies; Governance Principles; Standards and Procedures; Chapter 13 - Data Quality Governance; Metadata and Master Data interactions.
NEW QUESTION # 100
Before defining a Data Quality metric for a business-critical field, the team should first:
Answer: B
Explanation:
A meaningful Data Quality metric begins with the business requirement and intended use. Quality is fundamentally contextual: data is considered fit for purpose only relative to the activity, decision, report, or process that depends on it.
For example, a delivery address used for same-day logistics may require stricter timeliness and completeness thresholds than an address retained solely for historical analysis. Without understanding the business use, a numerical quality target becomes arbitrary.
The business requirement should identify what failure matters, which dimension applies, how quality will be measured, the acceptable threshold, who owns the requirement, and what response is expected when the threshold is missed.
DAMA-aligned public guidance follows this same logic by recommending that quality rules and dimensions be prioritized according to user needs and purpose.
Technology selection comes later. Profiling tools help measure data, but they cannot determine the business meaning of an acceptable result.
Governance and stewardship should approve the requirement, while Metadata Management should preserve the definition, rule, owner, and lineage.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Define Data Quality Requirements; Fitness for Purpose; Metrics; Thresholds; Business Rules.
NEW QUESTION # 101
An application that attempts to predict future outcomes through probability estimates is called:
Answer: B
Explanation:
Predictive analytics applies statistical, mathematical or machine-learning models to historical and current data in order to estimate future events, behaviours or outcomes. DAMA's treatment of analytics positions predictive analytics as a use of managed data in which patterns discovered from existing information are used to project what is likely to occur rather than merely describe what has already occurred. DMBOK2 explicitly places predictive analytics among advanced uses of data and notes the evolution of Business Intelligence from retrospective analysis toward predictive methods.
Probability estimation is therefore the distinguishing feature in this question. Descriptive analytics summarizes historical conditions-what happened. Predictive analytics estimates what may happen and commonly produces probabilities, scores, classifications or forecasts. "Reactive analytics," "dimensional analytics," and "just-in-time reporting" do not describe this statistical forecasting function.
Data Quality is fundamental to predictive performance. Missing values, inaccurate labels, duplicate entities, inconsistent categories, stale observations and biased source populations can materially distort estimated probabilities. Profiling should therefore assess distributions, completeness, outliers and consistency before modelling. Metadata must preserve definitions, lineage and transformation logic, while governed Master and Reference Data provide consistent classifications across training and operational datasets.
Predictive outputs themselves can also be monitored statistically for unexpected distribution shifts and unreasonable patterns.
Reference Topics: DAMA-DMBOK2 - Data Warehousing and Business Intelligence; Predictive Analytics; Chapter 13 - Profiling, Statistical Quality Control, Reasonableness, Accuracy and Completeness.
NEW QUESTION # 102
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