ShikenPASSは2008年に設立されましたが、現在、ハイパスDQ-1220ガイドトレントマテリアルの評判が高いため、この分野で主導的な地位にあります。 DQ-1220試験問題には、長年にわたって多くの同級生が続いていますが、これを超えることはありません。過去10年以来、成熟した完全なDQ-1220学習ガイドR＆Dシステム、顧客の情報安全システム、顧客サービスシステムを構築しています。有効なDQ-1220準備資料を購入したすべての候補者は、高品質のガイドトレント、情報の安全性、および最高のカスタマーサービスを利用できます。
| Certification Vendor: | DAMA International |
|---|---|
| Exam Name: | DAMA Data Quality (DQ) Exam |
| Exam Number: | DQ-1220 |
| Available Languages: | English |
| Related Certifications: | Certified Data Management Professional (CDMP) |
| Exam Format: | Multiple Choice Questions |
| Recommended Training: | DAMA-DMBOK Knowledge Guide |
| Exam Registration: | DAMA International Official Website |
| Sample Questions: | DAMA DQ-1220 Sample Questions |
| Exam Way: | Online proctored or authorized testing centers depending on provider |
| Pre Condition: | No strict prerequisites; familiarity with DAMA-DMBOK recommended |
| Official Syllabus URL: | https://www.dama.org |
ShikenPASSは、この分野ですでに世界中で有名なブランドになりました。これは、10年以上にわたって練習資料を編集してきており、実り多い成果が得られているためです。 DQ-1220無料のデモをダウンロードして、トレーニング資料に関する一般的なアイデアをお持ちください。 人によって好みが異なるため、PDF、オンラインアプリ、およびソフトウェアの3種類の異なるバージョンの模擬テストを用意しました。 最後になりましたが、お客様は模擬試験で試験スキルを向上させるだけでなく、試験の経験を積むことができます。 そして、あなたの成功は99％の高い合格率で100保証されています。
DAMA DQ-1220 認定試験は、データ品質管理分野で優れた実績を残したいと考える専門家にとって、最も求められている認定の一つです。試験は、組織の成功に重要なデータ品質分野での知識とスキルを評価するように設計されています。この認定は、データ管理協会（DAMA）によって提供され、データ品質の専門知識の基準として世界的に認められています。
質問 # 70
A customer moved house three months ago. The CRM still contains the customer's former address, although the record was originally correct when created. Which Data Quality dimension best describes the current problem?
正解：B
解説：
The issue is Currency. Currency concerns whether data remains sufficiently aligned with the current real- world state. The address was accurate when originally captured, but reality changed and the system was not updated.
This distinction matters because Data Quality can degrade over time without any technical error being introduced. Customer addresses, employment details, product prices, organizational structures, regulatory classifications, and contact details all have varying rates of change. A dataset that was reliable last year may no longer be fit for operational use today.
DAMA's current Chapter 13 revision explicitly identifies Currency as one of the nine standard Data Quality dimensions. Related DAMA-derived guidance also notes that real-world information changes and can therefore become outdated even when it was initially correct.
Organizations should establish refresh expectations according to business use. A marketing mailing list may tolerate a different update interval from an emergency-contact database.
Metadata should document refresh frequency and source authority, while quality monitoring can identify records exceeding acceptable age thresholds.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Currency; Accuracy; Timeliness; Data Aging; Monitoring; Fitness for Purpose.
質問 # 71
A customer table contains 50,000 records. Mandatory Tax Identification Number values are missing from
2,500 records. Which metric most directly measures the defect?
正解：A
解説：
The defect is measured using Completeness. Completeness evaluates whether all required records or values that should be present are actually present. In this scenario, the mandatory Tax Identification Number is absent from 2,500 of 50,000 customer records.
A straightforward attribute-level completeness metric would be calculated as the number of populated required values divided by the number expected. Therefore, 47,500 of 50,000 records contain the required value, producing a completeness result of 95%.
Completeness does not establish that populated values are accurate. A record may contain a Tax Identification Number and therefore pass the completeness rule while containing the wrong number. This separation between completeness and accuracy is essential when designing Data Quality scorecards. DAMA-aligned guidance defines completeness as the presence of required values and explicitly distinguishes it from factual correctness.
Governance should also determine whether the attribute is genuinely mandatory for every customer type.
Quality rules must reflect business applicability rather than blindly requiring every field for every record.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Completeness; Data Quality Metrics; Business Rules; Critical Data Elements; Scorecards.
質問 # 72
The DMBoK 'Environmental Factors hexagon' shows the relationship between:
正解：D
解説：
The DAMA-DMBOK2 Environmental Factors Hexagon illustrates the relationship between People, Process, and Technology. It provides a visual key for interpreting the Knowledge Area Context Diagrams and emphasizes that effective Data Management depends on the coordinated interaction of these three dimensions.
DMBOK2 places Goals and Principles at the center of the hexagon because they guide how people perform activities and how technology is selected and used. People-related factors include roles, responsibilities, organization, and culture. Process-related factors include activities, techniques, and methods. Technology- related factors include tools and the mechanisms that support Data Management deliverables.
This model is especially relevant to Data Quality. A profiling or cleansing platform alone cannot establish sustainable quality. The organization also needs accountable Data Owners and Data Stewards, repeatable issue-management and improvement processes, business-defined rules, and governance mechanisms.
Conversely, well-designed processes will fail if supporting technologies cannot monitor or enforce required controls.
Option A relates more closely to the structure of DMBOK Knowledge Area Context Diagrams. The DAMA Wheel represents the Knowledge Areas, while the Environmental Factors Hexagon specifically emphasizes the People-Process-Technology relationship.
Reference Topics: DAMA-DMBOK2 Chapter 1 - DAMA-DMBOK Framework; Environmental Factors Hexagon; People, Process and Technology; Chapter 13 - Data Quality Organization, Processes and Tools.
質問 # 73
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?
正解：B
解説：
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.
質問 # 74
The creation of overly complex enterprise integration over time is often a symptom of:
正解：C
解説：
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.
質問 # 75
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