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| Section | Weight | Objectives |
|---|---|---|
| Data Quality Governance | 15% | - Data Quality Roles and Responsibilities - Data Stewardship - Continuous Monitoring - Policies and Procedures |
| Data Quality Assessment and Measurement | 25% | - Root Cause Analysis - Profiling Techniques - Metrics and KPIs - Benchmarking |
| Data Quality Frameworks and Standards | 20% | - Other Industry Standards - DAMA DMBOK Framework - ISO 8000 (Data Quality) |
| Data Quality Fundamentals | 20% | - Data Quality in the Data Lifecycle - Definition of Data Quality - Dimensions of Data Quality (completeness, accuracy, consistency, timeliness, validity) |
| Data Quality Improvement | 20% | - Standardization - Validation Rules - Data Cleansing Techniques - Data Enrichment |
>> Certification DQ-1220 Sample Questions <<
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NEW QUESTION # 32
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?
Answer: C
Explanation:
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 Related DAMA-derived guidance also notes that real-world information changes and can therefore become outdated even when it was initially correct.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.
NEW QUESTION # 33
A Data Quality team has identified 500 defects across several domains. Which factor should have the strongest influence on remediation priority?
Answer: C
Explanation:
Remediation should principally be prioritized according to business impact and risk. Not all defects have equivalent consequences, even when they occur at similar frequencies.
A defect affecting regulatory reporting, customer payments, safety-critical operations, executive reporting, or high-value master data may require immediate remediation. A larger number of defects affecting a low- impact optional field may legitimately receive lower priority.
A robust prioritization model may consider financial loss, regulatory exposure, operational disruption, customer impact, reputational damage, number of dependent systems, recurrence rate, remediation cost, and whether a Critical Data Element is involved.
This risk-based approach prevents Data Quality programs from becoming simple defect-count reduction exercises. The objective is not merely to maximize the number of corrected records but to improve fitness for purpose where poor data creates material consequences.
Governance should approve prioritization criteria and resolve conflicts where different business areas assign different importance to the same issue. Metadata and lineage provide evidence about downstream dependencies and affected processes.
DAMA's revision of Chapter 13 adds a clearer Critical Data Element concept and clarifies responsibility within the Data Quality Improvement Lifecycle, reinforcing risk-based prioritization.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Issue Prioritization; Business Impact; Critical Data Elements; Risk; Remediation.
NEW QUESTION # 34
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: B
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 # 35
The seven Vs of Big Data are:
Answer: B
Explanation:
Within the DMBOK2 framing used by this certification material, the expanded characteristics are Volume, Velocity, Variety/Variability, Viscosity, Volatility, and Veracity. Option E is therefore the only choice that correctly contains the DAMA terms represented in the question. DMBOK-oriented study material explains that the original three Vs-Volume, Velocity, and Variety-were expanded to include Variability, Viscosity, Volatility, and Veracity in the broader characterization.
Volume concerns data quantity; Velocity concerns the rate of creation and processing; Variety/Variability concerns differing structures and changing representations; Viscosity describes difficulty in using or integrating the data; Volatility concerns how quickly usefulness or meaning changes; and Veracity addresses credibility and trustworthiness.
These characteristics have direct Data Quality implications. Increased variety creates semantic and structural consistency challenges. Velocity reduces the time available for traditional validation. Volatility affects currency and timeliness. Veracity is directly concerned with reliability.
DAMA's key point is that Big Data does not reduce the need for management discipline. Its scale and complexity increase the need for metadata, governance, automated profiling, lineage, and statistically driven quality controls.
Reference Topics: DAMA-DMBOK2 Big Data and Data Science - Big Data Characteristics; Volume; Velocity; Variety/Variability; Viscosity; Volatility; Veracity; Chapter 13 - Scalable Data Quality.
NEW QUESTION # 36
The implementation of a 'Master Data Repository', which is integrated across the enterprise, is an example of which integration approach?
Answer: B
Explanation:
An enterprise Master Data Repository serving multiple applications is a classic Hub-and-Spoke integration pattern. In this architecture, shared information is consolidated physically or virtually in a central hub, while participating applications interact with that hub rather than building a separate direct interface to every other application.
DAMA-DMBOK2 explicitly identifies Master Data Management hubs, Data Warehouses, Data Marts, and Operational Data Stores as familiar examples of data hubs. The hub-and-spoke model reduces the proliferation of point-to-point interfaces and can provide a consistent enterprise view of shared data.
This architecture is particularly appropriate for MDM because customer, product, supplier, location, or other master entities often need to be standardized, matched, governed, and redistributed across many systems. The central hub can apply survivorship rules, reference mappings, stewardship decisions, and quality controls before distributing trusted values.
Replication and Change Data Capture are techniques for moving or detecting changes in data, but neither describes the overall interaction topology. Publish-subscribe may operate alongside an MDM hub as a distribution mechanism, but the enterprise repository itself represents the hub within a hub-and-spoke architecture.
Reference Topics: DAMA-DMBOK2 Chapter 8 - Hub-and-Spoke; Integration Interaction Models; Chapter
10 - Master Data Management; Enterprise Master Data Hub; Data Quality Consistency.
NEW QUESTION # 37
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