많은 시간과 정신력을 투자하고 모험으로DAMA인증DQ-1220시험에 도전하시겠습니까? 아니면 우리DumpTOP 의 도움으로 시간을 절약하시겠습니까? 요즘 같은 시간인 즉 모든 것인 시대에 여러분은 당연히 DumpTOP의 제품이 딱 이라고 생각합니다. 그리고 우리 또한 그 많은 덤프판매사이트 중에서도 단연 일등이고 생각합니다. 우리 DumpTOP선택함으로 여러분은 성공을 선택한 것입니다.
DAMA DQ-1220 (데이터 품질) 인증 시험은 데이터 품질 분야에서 전문가의 지식과 기술을 검증하는 전 세계적으로 인정 된 인증입니다. 이 시험은 데이터 품질 관리와 관련된 주요 개념, 원칙 및 관행에 대한 후보자의 이해를 테스트하도록 설계되었습니다. 데이터 프로파일 링 및 클렌징에서 데이터 거버넌스 및 품질 측정에 이르기까지 광범위한 주제를 다룹니다.
DAMA DQ-1220 (데이터 품질) 인증 시험은 데이터 품질 관리에 대한 전문 지식을 보여주는 전 세계적으로 인정 된 인증입니다. 인증은 개념, 프로세스, 도구 및 기술을 포함하여 데이터 품질 분야의 데이터 관리 전문가의 기술과 지식을 테스트하도록 설계되었습니다.
우리DumpTOP 에서 여러분은 아주 간단히DAMA DQ-1220시험을 패스할 수 있습니다. 만약 처음DAMA DQ-1220시험에 도전한다면 우리의DAMA DQ-1220시험자료를 선택하여 다운받고 고부를 한다면 생가보다는 아주 쉽게DAMA DQ-1220시험을 통과할 수 있으며 무엇보다도 시험시의 자신감 충만에 많은 도움이 됩니다. 다른 자료판매사이트도 많겠지만 저희는 저희 자료에 자신이 있습니다. 우리의 시험자료는 모두 하이퀼러티한 문제와 답으로 구성되었습니다, 그리고 우리는 업데트를 아주 중요시 생각하기에 어느 사이트보다 더 최신버전을 보실 수 잇을것입니다. 우리의DAMA DQ-1220자료로 자신만만한 시험 준비하시기를 바랍니다. 우리를 선택함으로 자신의 시간을 아끼는 셈이라고 생각하시면 됩니다.DAMA DQ-1220로 빠른시일내에 자격증 취득하시고DAMAIT업계중에 엘리트한 전문가되시기를 바랍니다.
DAMA DQ-1220 인증 시험은 전 세계적으로 인정되며 업계에서 높이 평가됩니다. 인증은 개인이 고품질 데이터를 관리하고 유지하는 데 필요한 지식과 기술을 가지고 있음을 보여줍니다. 비즈니스 세계에서 데이터의 중요성이 높아짐에 따라이 인증은 매일 데이터를 사용하는 개인에게는 점점 더 가치가 높아지고 있습니다.
질문 # 122
A search engine database is populated by a web crawler or spider software that usually processes as:
정답：A
질문 # 123
An application that attempts to predict future outcomes through probability estimates is called:
정답：D
설명：
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.
질문 # 124
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?
정답：C
설명：
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.
질문 # 125
A Data Quality team wants to understand whether defect rates are improving month by month rather than viewing only the latest percentage. Which approach is most useful?
정답：A
설명：
Trend analysis is most useful because it shows how a quality metric changes over time rather than presenting a single isolated measurement.
A current defect rate of 4% has different implications depending on whether the previous months were 12%,
8%, and 6%, or 1%, 2%, and 3%. The first pattern indicates improvement; the second indicates deterioration.
Trend analysis therefore provides context for management decisions, helps determine whether remediation is producing sustained results, and can identify emerging degradation before thresholds are breached.
For stable, regularly measured processes, statistical control charts can add further value by distinguishing routine variation from signals that warrant investigation.
Metrics should be measured consistently over time. Changing definitions, populations, or calculation methods without documenting them can make apparent trends misleading.
Metadata should preserve metric definitions, threshold changes, and measurement logic to ensure comparability.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Monitoring; Trend Analysis; Statistical Control; Metrics; Continuous Improvement.
질문 # 126
Profiling shows that whenever Account_Status is "Closed", Closure_Date is normally populated. However,
4% of closed accounts have a null Closure_Date. What type of profiling has revealed this issue?
정답：C
설명：
This is cross-column dependency profiling. The issue cannot be identified merely by counting null values in Closure_Date because some nulls may be legitimate for active accounts. The defect becomes meaningful only when the relationship between two attributes is examined.
The implied business rule is: if Account_Status = Closed, then Closure_Date must be populated. Dependency profiling examines relationships among values and can reveal conditional completeness, inconsistent combinations, functional dependencies, and other multi-attribute patterns.
The next step is to confirm the inferred pattern with business stakeholders rather than automatically converting it into an enforced rule. There may be exceptional account categories where a closed status legitimately lacks a closure date.
Once approved, the rule should be documented as metadata, assigned to a Data Steward, measured against an agreed threshold, and monitored. Root-cause investigation can then determine whether missing dates arise from source-screen design, process bypass, conversion defects, or integration failures.
DAMA's revised Chapter 13 places Data Quality techniques in a more practical sequence and strengthens their linkage with Data Modeling and Metadata Management.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Profiling; Dependency Analysis; Completeness; Business Rules; Metadata Management.
질문 # 127
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DQ-1220최신버전자료: https://www.dumptop.com/DAMA/DQ-1220-dump.html