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To be eligible for the DAMA DQ-1220 Certification Exam, candidates must have a minimum of two years of experience in data management or a related field. They must also complete a training program that covers the exam's content, such as DAMA's Data Quality Fundamentals course. DQ-1220 exam itself is a computer-based test that consists of 100 multiple-choice questions. Candidates have two hours to complete the exam, and a passing score of 70% is required.
DAMA DQ-1220 Exam is a comprehensive certification that tests your knowledge and skills in managing data quality. It covers various topics related to data quality management, including data quality concepts, data quality assessment, data profiling, data cleansing, and data quality metrics. Passing the exam is a significant achievement that demonstrates your expertise in data quality management and can help you advance your career in data management.
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The DAMA DQ-1220 exam is intended for professionals who are involved in managing, designing, or developing data quality programs. It is also suitable for individuals who are responsible for ensuring that data is accurate, complete, and consistent across their organization. Candidates who pass the DAMA DQ-1220 Exam are recognized as experts in the field of data quality and are highly sought after by employers.
NEW QUESTION # 68
Data Staging areas are populated from source databases using extract functions, transformed and then:
Answer: B
Explanation:
The conventional flow is Extract, Transform, and Load (ETL). Data is extracted from source systems, placed in a staging environment where required transformations are performed, and then loaded into the target Data Warehouse. Consequently, the transformed staging data is made ready and loaded into the Data Warehouse.
DAMA-oriented training material presents this exact progression.
The staging area provides a controlled environment in which source data can be integrated without imposing transformation workloads directly on operational systems. Typical activities include datatype conversion, standardization, code translation, matching, deduplication, validation, derivation, consolidation, and handling rejected records.
From a Data Quality perspective, staging is especially important because many quality controls can be applied before data reaches the analytical repository. Profiling can identify unexpected distributions, invalid values, missing records, duplicate entities, referential-integrity defects, and inconsistent reference values. Failed records can then be quarantined or remediated according to governed rules.
Although metrics and profiling may occur in staging, they are supporting activities rather than the final destination in the ETL sequence. Likewise, metadata repositories document the transformation rather than serving as the main target for the transformed business data.
Reference Topics: DAMA-DMBOK2 - Data Warehousing and Business Intelligence; ETL; Staging Areas; Data Integration; Chapter 13 - Profiling, Cleansing, Standardization and Validation.
NEW QUESTION # 69
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?
Answer: C
Explanation:
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.
NEW QUESTION # 70
A lineage data tool provides:
Answer: B
Explanation:
A Data Lineage tool provides the capture and maintenance of source structures for each attribute on the data model. Lineage connects modeled data elements to their originating structures and records how those elements move and transform through downstream systems. DAMA-related practice material identifies this specific capability as the correct function of lineage tooling.
At attribute level, lineage may identify a source table and column, transformation logic, staging structures, intermediate mappings, target columns, and ultimately the report or analytical object in which the value appears. This information allows organizations to answer fundamental questions such as: Where did this value originate? What transformations were applied? Which systems depend on this field? What downstream assets will be affected if its definition changes?
For Data Quality, lineage is a critical diagnostic capability. If a value becomes inaccurate after a transformation, lineage helps isolate the point of failure and distinguish source-system defects from integration or reporting defects. It also supports impact analysis before quality-rule changes are deployed.
Metadata Management normally provides the repository and technical structures that preserve lineage.
Governance establishes ownership and documentation standards, while Data Stewards use lineage to understand business impact and coordinate remediation.
The other choices do not describe recognized lineage functions.
Reference Topics: DAMA-DMBOK2 - Metadata Management; Data Lineage; Data Architecture; Source- to-Target Mapping; Chapter 13 - Issue Investigation and Root-Cause Analysis.
NEW QUESTION # 71
When a data quality team has more issues than they can manage, they should look to:
Answer: D
Explanation:
When the Data Quality issue backlog exceeds the team's practical capacity, the appropriate approach is to establish a program of quick wins targeting manageable fixes over a short period. The objective is to reduce immediate pressure, demonstrate measurable value, and create organizational momentum rather than attempting to attack every known defect simultaneously.
DAMA-DMBOK2 recommends a hybrid implementation approach: top-down support provides sponsorship, consistency, and resources, while bottom-up work identifies what is actually broken and produces incremental successes. It further emphasizes prioritization of remediation based on business impact, documented costs and benefits, and sustained issue-management operations.
Deleting old issues is arbitrary and can conceal unresolved business risk. Hiring additional personnel may increase capacity but does not correct weak prioritization. Data-entry validation is valuable preventive control, but it addresses only certain causes of defects.
Option D sounds plausible because continuous improvement is fundamental to Data Quality Management; however, the immediate problem in this scenario is an unmanageable backlog. Short, prioritized wins provide the tactical mechanism for regaining control while the broader continuous-improvement lifecycle continues.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Quality Implementation Guidelines; Prioritization; Remediation; Incremental Successes; Root-Cause Analysis; Continuous Improvement.
NEW QUESTION # 72
A Data Quality incident affects several downstream systems. What should be determined before individual teams independently correct their copies?
Answer: D
Explanation:
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.
NEW QUESTION # 73
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