Was wissen Sie über die DAMA DQ-1220 Zertifizierungsprüfungen? Als eine sehr populäre DAMA Zertifizierungsprüfung ist diese Prüfung sehr wichtig. Aber wenn Sie für die bessere Vorbereitung der DAMA DQ-1220 Prüfungen die Schulungsunterlagen finden, ist es nicht leicht für Sie eine sehr ausgezeichnetes Nachschlagebuch finden. Und Was können Sie machen? Es macht nichts. Wir EchteFrage Ihre Wünsche kennen und Ihre Bedürfnisse erfüllen bei Angeboten der besten Prüfungsfragen und Antworten zur DAMA DQ-1220 Zertifizierung.
| Certification Vendor: | DAMA International |
|---|---|
| Exam Name: | Data Quality |
| Exam Number: | DQ-1220 |
| Passing Score: | 70% |
| Available Languages: | English |
| Certificate Validity Period: | 3 years |
| Exam Duration: | 90 minutes |
| Exam Format: | Multiple Choice, Scenario-Based |
| Real Exam Qty: | 70-100 |
| Related Certifications: | CDMP Master CDMP Fellow DG-1220 Data Governance CDMP Associate DMF-1220 Data Management Fundamentals |
| Exam Price: | $311 USD (member), $411 USD (non-member) |
| Recommended Training: | Official CDMP Study Resources DAMA DMBOK Guide |
| 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/ |
Um jeder DAMA DQ-1220 Prüfungsunterlagen Benutzer einen bequemen Prozess zu haben, bieten wir Ihnen 3 Versionen von DAMA DQ-1220 Prüfungsunterlagen, nämlich PDF-, Online-, und Software-Version. Eine der Versionen kann für Sie taugen und Ihnen helfen, innerhalb der kürzesten Zeit DAMA DQ-1220 zu bestehen und die autoritativste internationale Zertifizierung zu erwerben!
Die DAMA DQ-1220-Prüfung deckt verschiedene Themen im Zusammenhang mit der Datenqualität ab, einschließlich Datenprofile, Datenbereinigung, Datengovernance, Datenintegration und Datenqualitätsmessung. Es wurde entwickelt, um die Fähigkeit einer Person zu testen, Probleme mit der Datenqualität zu identifizieren, zu analysieren und zu beheben sowie Datenqualitätslösungen zu implementieren. Die Prüfung ist ideal für Personen, die an Datenverwaltung, Datenverwaltung, Datenanalyse, Datenqualitätssicherung und verwandten Bereichen beteiligt sind. Das Bestehen der Prüfung zeigt ein hohes Maß an Fachwissen in der Datenqualität und kann zu besseren Karrieremöglichkeiten auf diesem Gebiet führen.
15. Frage
The ethics of data handling, center on several core concepts. They are:
Antwort: E
Begründung:
DAMA-DMBOK2 organizes Data Handling Ethics around three fundamental considerations: impact on people, potential for misuse, and the economic value of data. The text explains that data frequently represents individuals and is used in decisions that materially affect them; consequently, organizations have an ethical responsibility to ensure that data is reliable and appropriately managed. It also recognizes that data can be intentionally or unintentionally misused and that its economic value creates questions concerning ownership, access, rights, and permitted use.
These concepts directly intersect with Data Quality. Incorrect or misleading data may result in decisions that adversely affect customers, employees, patients, citizens, or other stakeholders. For that reason, accuracy, completeness, transparency, provenance, and appropriate interpretation are not merely technical characteristics-they can have ethical consequences.
Governance operationalizes these ethical principles through policies, decision rights, stewardship responsibilities, access rules, monitoring, and escalation procedures. Metadata Management supports transparency by documenting meaning, provenance, classification, and permitted use. Master Data Management also has an ethical dimension because consolidated person or customer records may create significant consequences when matching or identity-resolution errors occur.
Privacy and security are important controls, but they do not constitute DAMA's complete set of core ethical concepts.
Reference Topics: DAMA-DMBOK2 Chapter 2 - Data Handling Ethics; Impact on People; Potential for Misuse; Economic Value of Data; Chapter 13 - Accuracy, Reliability and Governance.
16. Frage
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?
Antwort: E
Begründung:
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.
17. Frage
A dashboard reports that Customer_Email Completeness is 98.5%, while the approved threshold is 99.5%.
What should the 98.5% figure be interpreted as?
Antwort: C
Begründung:
The 98.5% value is a measured Data Quality result, and because it is below the approved 99.5% threshold, it represents a threshold breach.
A Data Quality metric quantifies observed performance. A threshold establishes the level of performance the business has agreed is acceptable. These should not be confused. The metric answers how are we performing?; the threshold answers is that performance acceptable?
The 98.5% completeness result says nothing about whether the populated email addresses are accurate. A field can achieve high completeness while containing incorrect or invalid values. Other rules may therefore be required for email syntax, uniqueness, deliverability, or factual correctness.
A threshold breach should trigger a predefined response appropriate to the criticality of the data. This may involve investigation, escalation, remediation, trend review, or root-cause analysis.
Metrics, calculation logic, measurement population, thresholds, ownership, and escalation rules should be documented as metadata so dashboard results remain interpretable and reproducible.
DAMA's framework explicitly places metrics, error detection, cleansing, and quality management within Data Quality Management.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Quality Metrics; Thresholds; Scorecards; Completeness; Monitoring.
18. Frage
A customer database contains postal addresses but lacks latitude and longitude. The organization adds geographic coordinates obtained from a trusted external service. This activity is an example of:
Antwort: B
Begründung:
This is Data Enrichment. Enrichment enhances existing records by adding useful information derived from internal or external sources. The original postal address remains intact, while geographic coordinates are appended to make the customer record more useful for location analysis, logistics, service coverage, route planning, or segmentation.
Enrichment differs from cleansing. Cleansing corrects or standardizes defective data; enrichment adds information that was not previously present. A process may perform both-for example, standardizing an address before passing it to a geocoding service.
External enrichment introduces governance and quality considerations. The organization should assess source reliability, licensing and permitted usage, refresh frequency, match confidence, geographical precision, lineage, and whether enrichment values should be treated as authoritative or derived.
Metadata should record that latitude and longitude were obtained from an external geocoding process rather than directly supplied by the customer. Quality metrics may measure match rate, confidence, completeness, or positional accuracy.
DAMA identifies Data Quality Management as including error detection, cleansing, and enrichment strategies, while Metadata Management supports understanding of lineage, definitions, and usage.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Enrichment; Cleansing; Profiling; Metadata Lineage; External Data Sources.
19. Frage
Following the rollout of a data issue process, there have been no issues recorded in the first month. The reason for this might be:
Antwort: E
Begründung:
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.
20. Frage
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DQ-1220 Zertifikatsfragen: https://www.echtefrage.top/DQ-1220-deutsch-pruefungen.html