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| Section | Objectives |
|---|---|
| Topic 1: Data Quality Management | - Roles and Responsibilities (Data Stewardship) - Data Quality Governance |
| Topic 2: Data Quality Operations | - Monitoring and Reporting - Data Quality Tools and Technologies |
| Topic 3: Data Quality Fundamentals | - Definition of Data Quality - Data Quality Dimensions (Accuracy, Completeness, Consistency, Timeliness, Validity, Uniqueness) |
| Topic 4: Data Quality Assessment | - Data Quality Metrics and KPIs - Data Profiling Techniques |
| Topic 5: Data Quality Improvement | - Root Cause Analysis - Data Cleansing Methods |
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NEW QUESTION # 116
A source field stores weight in pounds, while the target system requires kilograms. The integration specification should contain:
Answer: C
Explanation:
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.
NEW QUESTION # 117
A CRM contains separate records for "Jonathan Smith", "Jon Smith", and "J. Smith". Investigation confirms that all three records represent the same customer. Which Data Quality dimension is primarily affected?
Answer: C
Explanation:
The primary issue is Uniqueness because more than one record represents the same real-world entity.
Uniqueness evaluates whether unintended duplicate instances exist within a dataset or across integrated datasets.
Duplicate customer records can arise because of spelling variation, abbreviations, different contact details, weak matching rules, or inconsistent identifier capture. Exact duplicate detection alone is therefore insufficient. Effective entity resolution frequently requires probabilistic or deterministic matching across names, addresses, identifiers, telephone numbers, and other attributes.
DAMA-aligned Data Quality frameworks define uniqueness in terms of avoiding duplicate representation of the same entity. They also recognize that duplicate records may differ in individual attribute values while still representing the same person or object.
The issue is closely related to Master Data Management. An MDM process may match the three source records, establish that they belong to one customer, and apply survivorship rules to construct an authoritative master representation.
The correct remediation should also address the source process that allowed duplicates to be created; otherwise cleansing will merely remove symptoms while new duplicates continue to appear.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Uniqueness; Matching; De-duplication; Chapter 10 - Master Data Management; Entity Resolution.
NEW QUESTION # 118
A Data Quality process has remained within statistical control limits for six months, but management wants the average defect rate reduced further. What is the most appropriate conclusion?
Answer: D
Explanation:
A process can be statistically stable yet still perform at an unacceptable quality level. Statistical control means that variation is predictable within the established process; it does not mean that the average level of defects satisfies business expectations.
For example, a process may consistently produce a 3% defect rate with very little month-to-month variation.
If the business requirement is below 0.5%, the process is stable but incapable of meeting the desired quality level without improvement.
The appropriate response is therefore deliberate process improvement aimed at changing the underlying process and reducing its baseline defect rate. This differs from reacting to random individual observations within normal control limits.
After improvement, new performance data should be collected and control parameters reevaluated once the revised process reaches a stable state.
DAMA's revised Chapter 13 explicitly maps Shewhart and Deming improvement-cycle stages to Data Quality processes and preserves Statistical Process Control as supporting material.
The central principle is that control and capability are different questions: control asks whether the process is stable; business quality requirements determine whether that stable performance is good enough.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Statistical Process Control; Process Stability; Continuous Improvement; Shewhart/Deming Cycle; Thresholds.
NEW QUESTION # 119
A search engine database is populated by a web crawler or spider software that usually processes as:
Answer: D
NEW QUESTION # 120
The goals of data storage and operations are:
Answer: D
Explanation:
DAMA-DMBOK2 states three principal goals for Data Storage and Operations: managing data availability throughout the lifecycle, ensuring the integrity of data assets, and managing the performance of data transactions. Option A corresponds directly to those goals.
Availability means that required data and database services are accessible when business processes need them. This requires capacity planning, backup and recovery, business continuity, monitoring, and resilient infrastructure.
Integrity means protecting data assets from corruption or inappropriate alteration and maintaining reliable technical structures. Operational controls such as recovery procedures, transaction management, and controlled changes contribute directly to integrity.
Performance concerns the efficiency and responsiveness of database transactions and related storage operations. Capacity, cache behaviour, indexing, workload patterns, and system resources must therefore be monitored.
Glossary management belongs primarily to Metadata Management, while authorized access is principally a Data Security responsibility. User experience is not one of the stated core objectives for this knowledge area.
The relationship to Data Quality is direct: technically corrupted or unavailable information cannot be fit for purpose even when its business definitions are correct.
Reference Topics: DAMA-DMBOK2 Chapter 6 - Goals and Principles of Data Storage and Operations; Availability; Integrity; Performance; Chapter 13 - Data Reliability and Timeliness.
NEW QUESTION # 121
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