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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Quality Frameworks and Standards | 20% | - ISO 8000 (Data Quality) - Other Industry Standards - DAMA DMBOK Framework |
| Topic 2: Data Quality Governance | 15% | - Data Stewardship - Continuous Monitoring - Policies and Procedures - Data Quality Roles and Responsibilities |
| Topic 3: Data Quality Improvement | 20% | - Data Enrichment - Standardization - Validation Rules - Data Cleansing Techniques |
| Topic 4: Data Quality Assessment and Measurement | 25% | - Root Cause Analysis - Metrics and KPIs - Profiling Techniques - Benchmarking |
| Topic 5: Data Quality Fundamentals | 20% | - Data Quality in the Data Lifecycle - Dimensions of Data Quality (completeness, accuracy, consistency, timeliness, validity) - Definition of Data Quality |
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NEW QUESTION # 108
An address-validation service reports that 96% of customer addresses conform to an approved postal format.
What does this metric demonstrate most directly?
Answer: C
Explanation:
The metric demonstrates Validity because it measures whether address values conform to a defined format or standard.
Validity checks whether data satisfies specified rules concerning datatype, pattern, permitted values, ranges, or structures. Postal formatting rules might require approved postcode structure, valid country-code syntax, or required address components.
The metric does not prove that the address belongs to the correct customer. A perfectly formatted address may still be factually wrong. That would be an Accuracy problem.
DAMA-aligned government guidance defines validity in terms of conformity to expected format, type, range, and other predefined rules.
A mature address-quality program may combine validity with verification against authoritative postal reference data, duplication checks, completeness rules, and currency monitoring.
Quality scorecards should therefore label the metric precisely rather than calling the result a generic "accuracy score." Reference Topics: DAMA-DMBOK2 Chapter 13 - Validity; Accuracy; Data Quality Metrics; Address Quality; Reference Data.
NEW QUESTION # 109
Profiling reveals that an Age field has a minimum value of -7 and a maximum value of 263. What should the Data Quality team do first?
Answer: C
Explanation:
The values should first be treated as potential exceptions requiring investigation. Profiling identifies anomalies; it does not automatically prove that every unusual value is incorrect or specify the appropriate remediation.
Negative age and 263 years are highly improbable for a conventional person-age attribute, suggesting a validity or reasonableness defect. However, the Data Quality team should confirm the business definition, unit of measure, derivation logic, source mappings, special-value conventions, and intended population before changing the records.
Blindly replacing values with an average introduces fabricated data and destroys traceability. Deleting the records may also remove legitimate information elsewhere in the record. Increasing the datatype range addresses technical storage rather than semantic correctness.
A disciplined Data Quality process moves from profiling to rule validation, issue assessment, root-cause analysis, remediation, and monitoring. The business rule may ultimately specify an acceptable age range, but that rule should be governed and documented rather than inferred from individual anomalies.
Metadata is essential because it should describe whether Age is stored directly, calculated from Date of Birth, or encoded using another convention.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Profiling; Validity; Reasonableness; Root-Cause Analysis; Data Quality Rules; Metadata.
NEW QUESTION # 110
A customer table contains 50,000 records. Mandatory Tax Identification Number values are missing from
2,500 records. Which metric most directly measures the defect?
Answer: C
Explanation:
The defect is measured using Completeness. Completeness evaluates whether all required records or values that should be present are actually present. In this scenario, the mandatory Tax Identification Number is absent from 2,500 of 50,000 customer records.
A straightforward attribute-level completeness metric would be calculated as the number of populated required values divided by the number expected. Therefore, 47,500 of 50,000 records contain the required value, producing a completeness result of 95%.
Completeness does not establish that populated values are accurate. A record may contain a Tax Identification Number and therefore pass the completeness rule while containing the wrong number. This separation between completeness and accuracy is essential when designing Data Quality scorecards. DAMA-aligned guidance defines completeness as the presence of required values and explicitly distinguishes it from factual correctness.
Governance should also determine whether the attribute is genuinely mandatory for every customer type.
Quality rules must reflect business applicability rather than blindly requiring every field for every record.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Completeness; Data Quality Metrics; Business Rules; Critical Data Elements; Scorecards.
NEW QUESTION # 111
Big data management requires:
Answer: A
Explanation:
DAMA-DMBOK2 establishes the guiding principle that Big Data management requires more discipline than relational data management. The reason is not simply data volume. Big Data environments combine large volumes with substantial variation in structure, source, velocity, semantics, and reliability. These characteristics increase the probability of uncontrolled duplication, inconsistent interpretation, poor provenance, inappropriate use, and undetected quality defects. DAMA's Big Data guidance specifically emphasizes disciplined management of metadata describing Big Data sources, their origins, and their value.
From a Data Quality perspective, scale does not make data fit for purpose. Large datasets still require defined quality expectations, profiling, monitoring, lineage, controlled transformations, ownership, and governance.
Metadata becomes particularly important because analysts must understand where datasets originated, how they were transformed, their meaning, and whether they are appropriate for a particular analytical objective.
Data Governance must consequently establish accountability, acceptable-use policies, security requirements, quality expectations, and escalation mechanisms. Statistical and automated controls are also essential because manual inspection becomes impractical at Big Data scale.
Neither larger budgets nor data-science certification substitutes for sound data-management discipline.
Likewise, reducing controls because data is large creates greater-not lower-operational and analytical risk.
Reference Topics: DAMA-DMBOK2 - Big Data and Data Science; Big Data Management Principles; Metadata Management; Data Governance; Chapter 13 - Fitness for Purpose and Data Quality Controls.
NEW QUESTION # 112
In data modelling practice, entities are linked by:
Answer: A
Explanation:
In a data model, entities are linked by relationships. An entity represents a distinguishable business concept or thing about which the organization stores information, while a relationship expresses how one entity is associated with another.
For example, a Customer places an Order, an Order contains Order Lines, and a Product appears on an Order Line. Relationships therefore capture business semantics rather than merely physical implementation details.
Cardinality is an important characteristic of a relationship because it specifies how many instances of one entity may or must be associated with instances of another; however, cardinality is not itself the general mechanism by which entities are linked. DAMA-oriented modelling material identifies relationships as the correct linkage concept.
Indexes and triggers are physical database mechanisms. Processes describe business activities and may interact with entities, but they do not define entity-to-entity structure within a data model.
The Data Quality implications are significant. Properly defined relationships establish expectations for referential integrity. For example, an Order referencing a nonexistent Customer indicates an integrity defect.
Profiling can test orphaned foreign keys, invalid relationship cardinalities, and inconsistent associations.
Relationships and their cardinalities should therefore be documented as metadata and enforced through appropriate database, application, or quality controls.
Reference Topics: DAMA-DMBOK2 Chapter 5 - Entities, Relationships and Cardinality; Logical Data Modeling; Chapter 13 - Integrity and Consistency; Metadata Management.
NEW QUESTION # 113
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