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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Quality Concepts and Principles | 15% | - Data quality dimensions - Data quality in DMBOK framework - Definition and importance of data quality |
| Topic 2: Data Quality Assessment and Profiling | 25% | - Data profiling techniques - Defining quality rules and metrics - Assessment methods and tools |
| Topic 3: Tools, Techniques and Implementation | 20% | - Business value and ROI - Data quality in lifecycle management - Data quality tools and technologies |
| Topic 4: Data Quality Strategy and Governance | 20% | - Roles and responsibilities - Data quality strategy and policy - Integration with data governance |
| Topic 5: Data Quality Improvement | 20% | - Monitoring and continuous improvement - Root cause analysis - Data cleansing and standardization |
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NEW QUESTION # 10
A Data Quality team repeatedly corrects invalid postal codes in the warehouse, but the same errors reappear after every nightly load. What is the most appropriate long-term response?
Answer: A
Explanation:
The correct long-term response is to remove the root cause in the source or integration process. Repeated warehouse cleansing treats symptoms. If the defect is recreated every night, operational costs continue and downstream systems remain exposed until the cleansing process executes.
DAMA Data Quality practice emphasizes sustained improvement rather than repeated correction. The quality lifecycle therefore includes identifying defects, determining business impact, analyzing root causes, implementing corrective actions, and monitoring results. The current Chapter 13 revision specifically strengthens clarification of the Data Quality Improvement Lifecycle and responsibilities within it.
The underlying cause might be weak source validation, an incorrect source-to-target mapping, outdated Reference Data, transformation logic, or missing governance over postal-code rules.
Cleansing remains appropriate where historical data must be repaired or immediate downstream protection is required. However, preventive control should be introduced as close to the creation point as practical.
Metadata and lineage help trace the postal code through the data flow, while governance establishes who has authority to change the offending process.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Root-Cause Analysis; Remediation; Prevention; Cleansing; Data Integration and Lineage.
NEW QUESTION # 11
What are the types of models that are modelled in data models?
Answer: B
Explanation:
DAMA-DMBOK2 identifies four principal types of data that may be represented in data models: Category Information, Resource Information, Business Event Information, and Detail Transaction Information. Because options A, B, C and E are all legitimate categories, All of these is correct.
Category information classifies things-for example, classifying customers by market segment or products by size or colour. Resource information represents business resources required for operations, including entities such as Customer, Product, Supplier, Facility and Account. These resource entities frequently overlap strongly with Master and Reference Data Management.
Business event information records events created during operational processes, such as orders, invoices or withdrawals. Detail transaction information represents highly granular observations, including point-of-sale records, clickstream data, social-media interactions, sensor outputs and other high-volume events.
This classification matters to Data Quality because quality controls must reflect the semantics and lifecycle of the data being evaluated. Reference and master data require strong uniqueness, definition and consistency controls; event data require integrity and timeliness; high-volume detailed data often require automated profiling and statistical monitoring.
Data models also generate important metadata-definitions, relationships, domains and constraints-which becomes the basis for measurable Data Quality rules.
Reference Topics: DAMA-DMBOK2 Chapter 5 - Types of Data that are Modeled; Chapter 10 - Reference and Master Data; Chapter 13 - Data Quality Rules and Dimensions; Metadata Management.
NEW QUESTION # 12
A customer's date of birth is stored as 12/04/1980. The value conforms to the database datatype and accepted date format, but the customer's verified date of birth is 21/04/1980. Which Data Quality dimension is primarily violated?
Answer: D
Explanation:
The primary failure is Accuracy. Accuracy concerns whether a stored value correctly represents the real- world entity, event, or fact it is intended to describe. The recorded date is syntactically acceptable and conforms to the expected datatype and format, so it may satisfy Validity while still being factually wrong.
This distinction is fundamental in Data Quality assessment. A validity rule can determine whether a value is structurally permissible-for example, whether a date conforms to an approved pattern or whether a month lies between 1 and 12. It cannot by itself prove that the date belongs to the correct person. Government guidance based on DAMA dimensions makes the same distinction: a value can be valid while remaining inaccurate.
The strongest accuracy control would compare the value with an authoritative or independently verified source. Metadata should document the authoritative source and lineage, while governance should assign responsibility for resolving discrepancies.
The scenario therefore demonstrates why Data Quality programs must avoid assuming that format compliance equals correctness.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Accuracy; Validity; Authoritative Sources; Data Quality Rules; Metadata Lineage.
NEW QUESTION # 13
Which of the following is a reason why organisations do not dispose of non-value-adding information?
Answer: E
Explanation:
The correct answer is storage is cheap and easily expanded. DAMA-DMBOK2 discusses retention and disposal as important lifecycle-management responsibilities. Although information that no longer provides business, legal, regulatory, historical, or evidentiary value should normally be disposed of according to approved retention policies, organizations often postpone disposal because modern storage appears inexpensive and technically easy to expand.
This reasoning is deceptive. The acquisition cost of storage is only one component of total information- management cost. Retaining unnecessary information also increases backup requirements, recovery time, discovery obligations, privacy exposure, security risk, metadata-management effort, migration complexity, and the volume of information that must be governed. DMBOK2 therefore stresses that non-value-adding information should not be retained merely because storage capacity is readily available.
From a Data Quality perspective, excessive retention also increases the population of obsolete, redundant, and potentially inconsistent information. This makes profiling, lineage analysis, master-data reconciliation, and authoritative-source identification more difficult.
A sound governance program consequently combines retention schedules, legal requirements, metadata classification, defensible disposal procedures, and clear accountability so that data is retained for legitimate reasons rather than technological convenience.
Reference Topics: DAMA-DMBOK2 - Document and Content Management; Retention and Disposal; Information Lifecycle; Data Governance; Chapter 13 - Data Quality and Obsolete Data.
NEW QUESTION # 14
An application that attempts to predict future outcomes through probability estimates is called:
Answer: B
Explanation:
Predictive analytics applies statistical, mathematical or machine-learning models to historical and current data in order to estimate future events, behaviours or outcomes. DAMA's treatment of analytics positions predictive analytics as a use of managed data in which patterns discovered from existing information are used to project what is likely to occur rather than merely describe what has already occurred. DMBOK2 explicitly places predictive analytics among advanced uses of data and notes the evolution of Business Intelligence from retrospective analysis toward predictive methods.
Probability estimation is therefore the distinguishing feature in this question. Descriptive analytics summarizes historical conditions-what happened. Predictive analytics estimates what may happen and commonly produces probabilities, scores, classifications or forecasts. "Reactive analytics," "dimensional analytics," and "just-in-time reporting" do not describe this statistical forecasting function.
Data Quality is fundamental to predictive performance. Missing values, inaccurate labels, duplicate entities, inconsistent categories, stale observations and biased source populations can materially distort estimated probabilities. Profiling should therefore assess distributions, completeness, outliers and consistency before modelling. Metadata must preserve definitions, lineage and transformation logic, while governed Master and Reference Data provide consistent classifications across training and operational datasets.
Predictive outputs themselves can also be monitored statistically for unexpected distribution shifts and unreasonable patterns.
Reference Topics: DAMA-DMBOK2 - Data Warehousing and Business Intelligence; Predictive Analytics; Chapter 13 - Profiling, Statistical Quality Control, Reasonableness, Accuracy and Completeness.
NEW QUESTION # 15
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