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The DAMA DQ-1220 exam covers various topics related to data quality, such as data profiling, data cleansing, data governance, and data integration. It also emphasizes the importance of data quality in business decision-making and how it can impact an organization's overall success. DQ-1220 exam is designed for professionals in the data management field, including data analysts, data scientists, data architects, and data quality professionals.
DAMA DQ-1220 (Data Quality) Certification Exam is a professional certification exam that validates the knowledge and skills of individuals working in the field of data quality management. Data Quality certification exam is designed for data professionals who are responsible for ensuring the accuracy, completeness, and consistency of organizational data. DQ-1220 Exam covers a wide range of topics related to data quality management, including data profiling, data cleansing, data governance, and data quality measurement.
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Preparing for the DAMA DQ-1220 exam involves studying the DAMA Data Management Body of Knowledge (DMBoK), which covers all aspects of data management. Additionally, there are numerous study materials and practice exams available online to help individuals prepare for the exam. Taking the DAMA DQ-1220 Exam is a great way for data professionals to validate their expertise and gain recognition in the industry.
NEW QUESTION # 109
What are the types of models that are modelled in data models?
Answer: A
Explanation:
DAMA-DMBOK2 identifies four principal types of data that may be represented in data models: Category Information, Resource Information, Business Event Information, and Detail Transaction Information. Because options A, B, C and E are all legitimate categories, All of these is correct.
Category information classifies things-for example, classifying customers by market segment or products by size or colour. Resource information represents business resources required for operations, including entities such as Customer, Product, Supplier, Facility and Account. These resource entities frequently overlap strongly with Master and Reference Data Management.
Business event information records events created during operational processes, such as orders, invoices or withdrawals. Detail transaction information represents highly granular observations, including point-of-sale records, clickstream data, social-media interactions, sensor outputs and other high-volume events.
This classification matters to Data Quality because quality controls must reflect the semantics and lifecycle of the data being evaluated. Reference and master data require strong uniqueness, definition and consistency controls; event data require integrity and timeliness; high-volume detailed data often require automated profiling and statistical monitoring.
Data models also generate important metadata-definitions, relationships, domains and constraints-which becomes the basis for measurable Data Quality rules.
Reference Topics: DAMA-DMBOK2 Chapter 5 - Types of Data that are Modeled; Chapter 10 - Reference and Master Data; Chapter 13 - Data Quality Rules and Dimensions; Metadata Management.
NEW QUESTION # 110
One way of defining ethics is:
Answer: C
Explanation:
The correct principle is "Doing it right when no one is looking." The statement captures the essential distinction between ethical behaviour and mere compliance. Ethical conduct does not depend on observation, enforcement, or the probability of being caught; it requires responsible action because the action itself is appropriate. The answer wording is presented directly in the PDF on page 4.
DAMA-DMBOK2 treats data ethics as an essential component of professional Data Management because organizations routinely make choices about collecting, combining, analyzing, retaining, sharing, and monetizing data. A technically permissible activity may still create inappropriate effects on individuals or expose data to potential misuse.
Data ethics therefore extends beyond security and regulatory compliance. It requires consideration of the impact on people, potential for misuse, and economic value of data. Ethical handling also requires transparency concerning data provenance, intended use, quality limitations, and the consequences of decisions based on data.
Data Quality has an ethical dimension as well. Knowingly using inaccurate, incomplete, misleading, or poorly understood data in consequential decisions can harm customers and other stakeholders. Governance should therefore establish not only compliance controls but also principles for responsible use.
Reference Topics: DAMA-DMBOK2 Chapter 2 - Data Handling Ethics; Ethical Principles; Impact on People; Potential for Misuse; Chapter 13 - Reliable and Fit-for-Purpose Data.
NEW QUESTION # 111
When a data quality team has more issues than they can manage, they should look to:
Answer: A
Explanation:
When the Data Quality issue backlog exceeds the team's practical capacity, the appropriate approach is to establish a program of quick wins targeting manageable fixes over a short period. The objective is to reduce immediate pressure, demonstrate measurable value, and create organizational momentum rather than attempting to attack every known defect simultaneously.
DAMA-DMBOK2 recommends a hybrid implementation approach: top-down support provides sponsorship, consistency, and resources, while bottom-up work identifies what is actually broken and produces incremental successes. It further emphasizes prioritization of remediation based on business impact, documented costs and benefits, and sustained issue-management operations.
Deleting old issues is arbitrary and can conceal unresolved business risk. Hiring additional personnel may increase capacity but does not correct weak prioritization. Data-entry validation is valuable preventive control, but it addresses only certain causes of defects.
Option D sounds plausible because continuous improvement is fundamental to Data Quality Management; however, the immediate problem in this scenario is an unmanageable backlog. Short, prioritized wins provide the tactical mechanism for regaining control while the broader continuous-improvement lifecycle continues.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Quality Implementation Guidelines; Prioritization; Remediation; Incremental Successes; Root-Cause Analysis; Continuous Improvement.
NEW QUESTION # 112
A report displaying birth date contains possible, but incorrect values. What is a possible explanation?
Answer: C
Explanation:
The critical wording is "possible, but incorrect values." This describes values that satisfy basic syntactic or domain validation-they look like legitimate dates-but do not accurately represent the real-world attribute defined by the field.
If two systems contribute data and one maps marriage date into the birth-date field, the resulting values can be perfectly valid calendar dates while being semantically incorrect as birth dates. This is principally an Accuracy defect, because DAMA defines accuracy in terms of how correctly data represents the real-world object or event it is intended to describe. It may also expose a consistency and integration-mapping problem between source systems. DAMA's quality framework distinguishes accuracy from completeness: data can be populated and formally valid while still being factually wrong.
Missing values would primarily produce a Completeness defect rather than populated-but-incorrect values.
Two correctly mapped systems would not inherently explain the problem. An offset or technical date representation could create transformation problems, but the scenario most directly illustrates semantic mis- mapping between data elements.
The appropriate remediation is therefore not simple cleansing alone. Metadata mappings, source-to-target specifications, lineage, business definitions, and integration rules should be corrected at the root cause.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Accuracy, Completeness and Consistency; Root-Cause Remediation; Data Profiling; Metadata Management; Data Integration and Interoperability.
NEW QUESTION # 113
An application that attempts to predict future outcomes through probability estimates is called:
Answer: A
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 # 114
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