私たちに知られているように、適切な学習計画はすべての人々にとって非常に重要です。競争力を高めるために、学習計画を立てる必要があります。 DQ-1220の実際の試験は、優れた学習計画の作成に役立つと考えています。 DQ-1220学習教材を使用して、限られた時間でモデルテストを行うことができます。モデルテストを完了すると、システムがパフォーマンスに応じてレポートを生成します。あなたがマスターしていない知識ポイントを知ることができます。 DQ-1220調査の質問からのレポートによる。そうすれば、DQ-1220試験に簡単に合格できます。
| Certification Vendor: | DAMA International |
|---|---|
| Exam Name: | Data Quality |
| Exam Number: | DQ-1220 |
| Related Certifications: | CDMP - Certified Data Management Professional |
| Exam Duration: | 60 minutes |
| Real Exam Qty: | 80 |
| Exam Format: | Multiple Choice |
| Certificate Validity Period: | No expiration for CDMP certificate (requires continuing education/renewal credits) |
| Available Languages: | English |
| Passing Score: | 70% |
| 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/ |
DQ-1220試験に参加する前に、試験を知りたい場合、弊社の公式サイトを訪問できます。そして、弊社のDQ-1220試験ガイドのデモをダウンロードすることは簡単で、便利です。クリックするだけ必要からです。後、弊社のDQ-1220資料はすべてDQ-1220試験に関わることがわかります。DQ-1220資料の全てのページはDQ-1220試験に関連しています。DQ-1220資料は素晴らしいものです。
この試験は、100の多肢選択問題から構成され、2時間の時間制限があります。問題は、データ品質の概念、原則、およびベストプラクティスに関する候補者の知識と理解をテストするように設計されています。試験は、データ品質評価、データ品質測定、データ品質改善、およびデータ品質管理フレームワークなどのトピックをカバーしています。DAMA DQ-1220認定は、キャリアの見通しを向上させ、データ品質管理の専門知識を証明することを目的とするプロフェッショナルにとって貴重な資産です。
質問 # 89
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?
正解：C
解説：
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.
質問 # 90
A customer is shown as "Active" in the CRM but "Closed" in the billing system at the same point in time.
Both systems use the same customer identifier. Which Data Quality dimension is primarily affected?
正解：A
解説：
The primary dimension affected is Consistency. Consistency evaluates whether equivalent information agrees across datasets, systems, representations, or related attributes when business rules require agreement.
The same customer identifier is represented with conflicting lifecycle statuses at the same time. Each value might individually satisfy its local datatype and permitted-value rules, so both could be valid in isolation.
However, unless a legitimate business rule explains the difference, the two representations are inconsistent.
Resolving the issue requires more than choosing one value arbitrarily. The organization must establish the authoritative source for customer status, understand whether the systems apply different definitions of
"Active" and "Closed," examine synchronization timing, and trace transformation or integration logic.
Metadata should document the business meaning and source authority, while lineage should reveal how status information is propagated.
Master Data Management may also be relevant where customer-state information is shared across multiple operational systems. Governance must determine ownership and conflict-resolution rules.
DAMA's revised Chapter 13 identifies Consistency among the standardized Data Quality dimensions and explicitly strengthens its relationship with Metadata, Reference/Master Data, and Data Integration.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Consistency; Authoritative Sources; Metadata Management; Master Data Management; Data Integration.
質問 # 91
The primary reason for identifying Critical Data Elements is to:
正解：A
解説：
Critical Data Elements are identified so that organizations can concentrate governance and Data Quality effort where failure would create the greatest business impact.
Not every attribute deserves the same level of profiling, monitoring, stewardship, lineage documentation, control, and remediation. Applying identical controls to every field would usually be economically inefficient and may divert resources away from information that supports regulation, financial reporting, key operations, customer outcomes, or strategic decisions.
The DMBOK2 maintenance revision explicitly adds and clarifies the concept of a Critical Data Element within Chapter 13.
Once a CDE is identified, the organization can define applicable quality dimensions, measurable rules, thresholds, ownership, authoritative sources, lineage, issue escalation, and monitoring frequency.
Criticality is contextual. An attribute may be critical to one process and relatively insignificant to another. The decision should therefore reflect documented business use and risk rather than technical prominence.
Metadata remains essential because it links the CDE to definitions, systems, lineage, owners, and quality controls.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Critical Data Elements; Prioritization; Business Impact; Risk; Data Governance; Metadata.
質問 # 92
A relationship that allows an address to be used by multiple people, and each person can have multiple addresses, can be resolved:
正解：E
解説：
The scenario describes a many-to-many relationship: one Person may use several Addresses, while one Address may be associated with several Persons. In relational modelling, this should be resolved using an associative entity-here, Person Address Usage-between Person and Address. The original many-to-many relationship is thereby replaced with two one-to-many relationships. DAMA-oriented references identify this approach directly under the addition of associative entities.
The associative entity normally contains foreign keys referencing both parent entities and may also contain attributes describing the association itself, such as address type, usage purpose, effective date, end date, or primary-address indicator.
Simply modifying the primary keys of Person or Address does not resolve the semantic relationship.
Changing foreign-key role names also changes terminology rather than cardinality. The term "partnership entity" is not the appropriate modelling construct.
This design has important Data Quality consequences. It permits explicit integrity constraints between the association and both parent entities, prevents ambiguous repeated columns, and allows the organization to validate rules such as valid effective periods and permitted address-use types.
Reference Topics: DAMA-DMBOK2 Chapter 5 - Relationships; Cardinality; Associative Entities; Resolving Many-to-Many Relationships; Chapter 13 - Referential Integrity and Consistency.
質問 # 93
A Data Quality incident affects several downstream systems. What should be determined before individual teams independently correct their copies?
正解：A
解説：
The organization should determine the root cause and authoritative remediation point before multiple teams independently modify downstream copies.
If a defective value originates in a source system and is distributed to five downstream applications, correcting those five copies independently may provide temporary relief but leaves the upstream defect intact.
The incorrect value may simply be redistributed again.
Lineage should be used to trace the defect through source systems, transformations, integration layers, Master Data hubs, warehouses, and reports. Once the origin is understood, governance can determine which system or process is authoritative and where correction should occur.
This approach minimizes inconsistent local fixes and reduces the risk that different teams apply incompatible interpretations of the same issue.
DAMA's Chapter 13 revision explicitly strengthens the relationship between Data Quality and Metadata Management, Reference/Master Data Management, Modeling, and Data Integration. Those connections are precisely what enable cross-system issue resolution.
Downstream correction may still be required after the authoritative source is fixed, but it should be coordinated.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Root-Cause Analysis; Issue Management; Metadata Lineage; Authoritative Sources; Data Integration.
質問 # 94
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