DAMAのDQ-1220試験トレントの指示に従って、準備期間を非常に短い時間で完了し、試験に合格することもできます。これにより、多くの時間とエネルギーを節約し、Data Quality準備トレントで生産性を高めることができます。 実際、あなたが進歩するための高効率な準備時間を保証する理由は、主に、当社JPTestKingのDQ-1220テストで学習プロセス中に顧客を集中させ、ターゲットを絞ることができるコンテンツとレイアウトの素晴らしい組織に起因します ブレインダンプ。 DQ-1220のData Quality試験準備の高い合格率は99％〜100％です。
| Certification Vendor: | DAMA International |
|---|---|
| Exam Name: | Data Quality |
| Exam Number: | DQ-1220 |
| Real Exam Qty: | 80 |
| Related Certifications: | CDMP - Certified Data Management Professional |
| Exam Duration: | 60 minutes |
| Available Languages: | English |
| Certificate Validity Period: | No expiration for CDMP certificate (requires continuing education/renewal credits) |
| Passing Score: | 70% |
| Exam Format: | Multiple Choice |
| Exam Price: | USD $400 (Full CDMP exam) |
| Sample Questions: | DAMA DQ-1220 Sample Questions |
| Exam Way: | Online proctored or Onsite testing center |
| Pre Condition: | No formal prerequisites; recommended experience in data management |
| Official Syllabus URL: | https://dama.org/certification/ |
学習者の学習条件はさまざまであり、多くの場合、DQ-1220学習問題を学習するためにインターネットにアクセスできない場合があります。学習者が自宅や会社を離れる場合、インターネットにリンクしてDQ-1220テストpdfを学習することはできません。しかし、あなたはオフラインで学ぶことができる私たちのAPPオンライン版を使用しています。初めてオンラインになる環境でDQ-1220学習質問を使用する場合のみ、後でオフラインで使用できます。したがって、DQ-1220試験の練習教材をどこでも心配する必要がないため、すべての学習者にとって非常に便利です。
DAMA DQ-1220試験は、データ品質管理に関連する広範なトピックをカバーしており、データプロファイリング、データクレンジング、データバリデーション、およびデータ標準化を含みます。試験はまた、データガバナンス、データプライバシー、およびデータセキュリティなどの重要な概念もカバーしています。この試験は、これらの概念を実世界のシナリオに適用する能力を候補者にテストするよう設計されており、データ管理分野の専門家にとって不可欠な認定資格となっています。
質問 # 87
A Data Quality team finds that 78% of customer complaints are caused by four defect categories out of twenty recorded categories. Which tool is most appropriate for prioritizing improvement activity?
正解：B
解説：
Pareto analysis is the appropriate technique because it identifies the relatively small number of causes responsible for a large proportion of observed problems.
The team should arrange defect categories in descending order by frequency or business impact and calculate their cumulative contribution. If four categories account for 78% of complaints, prioritizing those categories is likely to generate substantially greater benefit than spreading equal effort across all twenty.
The value of Pareto analysis in Data Quality is not that every problem follows an exact 80/20 relationship.
Rather, it provides an evidence-based mechanism for focusing scarce remediation resources on the defect classes that generate the most significant outcomes.
Frequency should not be the only prioritization criterion. A rare defect could produce severe regulatory, safety, financial, or reputational consequences. Governance should therefore combine volume analysis with business impact and risk.
Once priority defects are selected, the team should perform root-cause analysis and introduce preventive controls instead of merely fixing individual records.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Statistical Quality Tools; Pareto Analysis; Issue Prioritization; Business Impact; Root-Cause Remediation.
質問 # 88
What is the ideal data role combination for assignments per subject area and even to each entity within subject areas?
正解：E
解説：
The ideal combination is a Data Architect and a Data Steward. DAMA-DMBOK2 states explicitly that, ideally, both roles should be assigned to each subject area and even to individual entities within a subject area.
This arrangement integrates architectural control with business accountability.
The Data Architect contributes enterprise-wide structural knowledge: subject-area boundaries, entities, relationships, integration requirements, architectural standards, and alignment with the broader Enterprise Data Architecture. The Data Steward contributes authoritative business knowledge and accountability for terminology, business rules, valid values, quality expectations, and appropriate use of the data.
DMBOK2 also describes the enterprise data model as something that should be developed and maintained jointly by Data Architects and Data Stewards working together in subject-area teams.
This pairing is especially valuable for Data Quality because structural correctness alone does not guarantee fitness for purpose. A technically valid model may still contain ambiguous definitions or inappropriate business rules. Conversely, business requirements without architectural discipline can create duplicated or inconsistent structures.
Working together, the Architect and Steward ensure that definitions, models, metadata, lineage, quality rules, and governance decisions remain aligned. DBAs, analysts, modellers, and business analysts are important contributing roles, but they do not provide the same combined architectural and governance accountability.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Data Architecture Governance; Subject Areas; Chapter 3
- Data Stewardship; Enterprise Data Models; Chapter 13 - Data Quality Accountability.
質問 # 89
A source field stores weight in pounds, while the target system requires kilograms. The integration specification should contain:
正解：C
解説：
The integration specification requires a documented transformation rule defining how pounds are converted to kilograms.
Source-to-target mappings should identify source attributes, target attributes, transformation logic, units, datatypes, validation expectations, and handling of exceptions. Without this metadata, different interfaces could apply different conversion factors or rounding rules and create inconsistent downstream values.
The rule should specify the approved conversion factor, precision, rounding method, treatment of nulls, and any acceptable source ranges. Testing should confirm that the transformed values remain within defined quality thresholds.
This illustrates the relationship between Data Integration, Metadata Management, and Data Quality that DAMA's current Chapter 13 revision makes more explicit.
The source value may be completely accurate in pounds while the target value becomes inaccurate through faulty transformation. Therefore, quality responsibility extends beyond original data capture.
Lineage should retain both the source and transformation information so downstream analysts can understand how the kilogram value was derived.
Reference Topics: DAMA-DMBOK2 - Data Integration and Interoperability; Source-to-Target Mapping; Transformation; Metadata Lineage; Accuracy.
質問 # 90
Examples of transformation include:
正解：C
解説：
Data transformation includes operations such as format changes, structural changes, semantic conversion, de- duplication, and re-ordering. These activities modify source information so that it conforms to the syntactic, structural, or semantic requirements of a target environment.
A format transformation may convert dates from DD/MM/YYYY to an ISO representation. Structural transformation may split, combine, flatten, or restructure attributes. Semantic conversion changes representation while preserving intended meaning-for example, translating a source status code into the standardized enterprise code. De-duplication identifies multiple records representing the same real-world entity, while re-ordering changes the sequence or organization of records or attributes. The listed combination aligns directly with DAMA-oriented transformation guidance.
The distinction from the distractors is important. Organizational change, infrastructure replacement, or application modernization may trigger data transformation, but they are not themselves data-transformation techniques. "Re-duplication" is also inconsistent with the objective of improving integrated datasets.
Transformation is strongly connected to Data Quality. Poorly specified conversion rules can create invalid values, truncate data, introduce semantic inconsistencies, or produce duplicate entities. Consequently, transformations should be documented through mappings and metadata, tested against quality rules, reconciled with source totals, and monitored for exceptions.
Reference Topics: DAMA-DMBOK2 Chapter 8 - Transformation and Mapping; ETL/ELT; Chapter 13 - Standardization, Cleansing, De-duplication and Validation.
質問 # 91
An effective Data Governance communication program should include the following:
正解：C
解説：
An effective Data Governance communication program should employ multiple complementary communication mechanisms, making All answers correct. Governance changes how people define, create, use, approve, and resolve issues with data; consequently, sustained adoption requires more than publishing policies.
Regular newsletters keep stakeholders aware of progress, decisions, metrics, and upcoming activities. A Data Governance Portal provides a persistent location for policies, standards, stewardship information, glossaries, issue processes, and supporting materials. Custom training develops the capabilities required for individuals to understand their specific governance responsibilities. Informal networking events help establish relationships across business and technical groups, which is particularly important when resolving data ownership and definition conflicts.
DAMA-DMBOK2 treats communication and organizational change as core implementation considerations because governance depends on participation across functions rather than on a single technical team.
Published CDMP material for this item identifies the combined response-newsletters, portal, training, and networking-as the intended answer.
For Data Quality, communication ensures that quality definitions, issue-management procedures, stewardship responsibilities, thresholds, and remediation decisions are understood and consistently applied.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Governance Communications; Organizational Change; Training; Data Governance Portal; Stewardship Engagement; Chapter 13 - Data Quality Culture.
質問 # 92
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