Unsere DAMA DQ-1220 Prüfungsunterlage (Data Quality) enthalten alle echten, originalen und richtigen Fragen und Antworten. Die Abdeckungsrate unserer DAMA DQ-1220 Unterlagen (Fragen und Antworten) (Data Quality) ist normalerweise mehr als 98%.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Tools, Techniques and Implementation | 20% | - Data quality tools and technologies - Data quality in lifecycle management - Business value and ROI |
| Topic 2: Data Quality Improvement | 20% | - Data cleansing and standardization - Root cause analysis - Monitoring and continuous improvement |
| Topic 3: Data Quality Assessment and Profiling | 25% | - Data profiling techniques - Defining quality rules and metrics - Assessment methods and tools |
| Topic 4: Data Quality Concepts and Principles | 15% | - Definition and importance of data quality - Data quality dimensions - Data quality in DMBOK framework |
| Topic 5: Data Quality Strategy and Governance | 20% | - Roles and responsibilities - Integration with data governance - Data quality strategy and policy |
>> DQ-1220 Übungsmaterialien <<
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34. Frage
A source application is modified so that a mandatory product identifier can no longer be left blank. Which type of Data Quality action is this?
Antwort: D
Begründung:
The application change is a preventive Data Quality control because it prevents a known defect from being created in the first place.
A mandatory-field control ensures that records cannot be accepted without the required Product Identifier.
This differs from a detective control, which would identify missing identifiers after records had already entered the system.
Preventive controls are generally preferable where the organization controls the point of data creation and the business rule is sufficiently clear. They reduce downstream cleansing, exception handling, reconciliation, and operational rework.
However, making the field technically mandatory is appropriate only if the business requirement genuinely applies to every relevant record. Governance and stewardship should confirm the rule before implementation.
If certain product categories legitimately lack the identifier, the validation should incorporate those conditions instead of enforcing an overly broad requirement.
The Product Identifier may also link the transaction to governed Product Master Data, making integrity and consistency important alongside completeness.
DAMA's Data Quality framework emphasizes defining rules, detecting defects, implementing improvement, and integrating quality with Governance and Master Data disciplines rather than relying solely on after-the- fact cleansing.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Preventive Controls; Completeness; Data Quality Improvement Lifecycle; Chapter 10 - Product Master Data; Governance.
35. Frage
Achieving security risk reduction in an organisation begins with developing what?
Antwort: B
Begründung:
Effective security risk reduction begins by understanding what data exists, how sensitive or critical it is, and where it resides. A classification model provides this foundation by classifying data concepts according to protection requirements and linking those concepts to their physical implementations. The certification material reflects this DAMA principle directly.
Data cannot be protected proportionately if the organization does not know whether it represents public information, internal operational information, personally identifiable information, financial data, intellectual property, regulated information, or another sensitive category. Classification allows the organization to apply appropriate controls according to risk rather than treating every data asset identically.
Once classification is established, security teams can define access requirements, encryption needs, monitoring, retention controls, masking, authorization policies, and other safeguards. Metadata is essential because classifications must ultimately be connected to actual databases, files, attributes, interfaces, and repositories.
A firewall alone does not provide data-level protection, and role classification by itself addresses only one dimension of security. Similarly, an Enterprise Data Model describes organizational data structures but does not replace security classification.
Reference Topics: DAMA-DMBOK2 Chapter 7 - Data Security; Data Classification; Risk Reduction; Sensitive Data Discovery; Metadata Management; Chapter 13 - Integrity and Controlled Use.
36. Frage
Three source systems provide different telephone numbers for the same customer. An MDM hub selects one number according to approved source-priority rules. This is an example of:
Antwort: D
Begründung:
The process is Survivorship. In Master Data Management, survivorship determines which value should become the preferred or authoritative representation when multiple source records contain conflicting values for the same attribute.
Rules may prioritize particular systems, use recency, trust scores, verification status, completeness, or combinations of factors. For example, a verified customer-service update might outrank an older marketing- system telephone number.
Survivorship occurs after or in conjunction with matching and entity resolution. The organization first determines that records from different sources represent the same real-world customer and then determines which attribute values should populate the mastered representation.
The process requires governance because the "best" value is a business decision, not merely a technical one.
Data Stewards should approve the rules, and metadata should record source priority, rule logic, and lineage.
DAMA's framework positions Reference and Master Data Management as the discipline responsible for ensuring consistent core entities across the organization.
Reference Topics: DAMA-DMBOK2 Chapter 10 - Master Data Management; Matching; Survivorship; Authoritative Values; Chapter 13 - Consistency and Accuracy.
37. Frage
The creation of overly complex enterprise integration over time is often a symptom of:
Antwort: D
Begründung:
The strongest indicator is the uncontrolled proliferation of multiple integration technologies. Enterprise integration environments often become progressively more complex when different projects independently adopt different ETL platforms, messaging products, APIs, middleware technologies, replication mechanisms, file-transfer approaches, and proprietary interfaces.
The problem is architectural fragmentation. Each integration technology introduces its own configuration methods, transformation logic, monitoring mechanisms, metadata, failure handling, security controls, support skills, and operational procedures. As the number of technologies increases, point-to-point dependencies multiply and the organization accumulates technical debt. This makes end-to-end lineage, troubleshooting, change-impact analysis, and consistent application of Data Quality rules significantly harder. The source question itself identifies this specific integration-management issue. DAMA-oriented exam material likewise identifies multiple integration technologies as the characteristic cause of an overly complex integration landscape.
DAMA-DMBOK2 therefore emphasizes managed Data Integration and Interoperability architecture, reusable patterns, common models, governed interfaces, and metadata describing transformations.
From a Data Quality perspective, uncontrolled integration technology can result in inconsistent transformations, duplicated cleansing rules, mismatched reference values, and poorly understood lineage.
Standardization reduces these risks by making movement and transformation processes more transparent and governable.
Reference Topics: DAMA-DMBOK2 Chapter 8 - Data Integration and Interoperability; Integration Architecture; Metadata and Lineage; Chapter 13 - Quality Controls Across Data Movement.
38. Frage
Mapping requirements and rules for moving data from source to target enables:
Antwort: B
Begründung:
Source-to-target mapping enables Transformation. DAMA-DMBOK2 treats mapping as closely synonymous with transformation because a mapping defines how data in one source structure will be converted into the structure, format, representation, or value required by the target.
A mapping specification typically identifies the source attribute, target attribute, extraction conditions, target population rules, intermediate staging transformations, calculations, lookup requirements, and any changes required to make the source data conform to the target representation. DMBOK2 specifically explains that mapping sources to targets involves defining the rules for transforming information from one location and format into another.
Extraction simply retrieves data from the source. Loading places data into the target. Transformation is the activity that applies structural, syntactic, semantic, or value-level modifications between those stages.
The Data Quality connection is substantial. Mappings may standardize dates, convert units, harmonize codes, resolve reference values, remove duplicates, or enforce business rules. If mapping metadata is incomplete or incorrect, the transformation process can introduce rather than correct quality defects.
For this reason, source-to-target mapping should be governed, version-controlled, documented as metadata, traceable through lineage, and validated against agreed business definitions and Data Quality requirements.
Reference Topics: DAMA-DMBOK2 Chapter 8 - Map Data Sources to Targets; Transformation; ETL
/ELT; Metadata Lineage; Chapter 13 - Data Cleansing and Standardization.
39. Frage
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