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| Certification Vendor: | DAMA International |
|---|---|
| Exam Name: | Data Quality |
| Exam Number: | DQ-1220 |
| Passing Score: | 70% |
| Exam Duration: | 90 minutes |
| Exam Price: | $311 USD (member), $411 USD (non-member) |
| Exam Format: | Scenario-Based, Multiple Choice |
| Certificate Validity Period: | 3 years |
| Related Certifications: | CDMP Master DG-1220 Data Governance CDMP Associate DMF-1220 Data Management Fundamentals CDMP Fellow |
| Real Exam Qty: | 70-100 |
| Available Languages: | English |
| Recommended Training: | Official CDMP Study Resources DAMA DMBOK Guide |
| Exam Registration: | Testing Partner: Kryterion DAMA Official Registration |
| Sample Questions: | DAMA DQ-1220 Sample Questions |
| Exam Way: | In-person at authorized test centers or remote online proctored |
| Pre Condition: | Recommended: DMF-1220 (Data Management Fundamentals) or equivalent knowledge; 2+ years of data management experience |
| Official Syllabus URL: | https://dama.org/certification/cdmp/ |
Solutions is commented Test4Cram to ace your Data Quality (DQ-1220) exam preparation and enable you to pass the final DAMA DQ-1220 exam with flying colors. To achieve this objective Exams. Solutions is offering updated, real, and error-free DQ-1220 Certification Exam questions in three easy-to-use and compatible formats. These Data Quality (DQ-1220) exam questions formats will help you in preparation.
To take the DAMA DQ-1220 exam, you must have a solid understanding of data quality management principles and techniques. You should also have experience working with data and be familiar with data quality tools and technologies. DQ-1220 Exam is ideal for individuals who want to advance their career in data management and demonstrate their expertise in data quality management.
NEW QUESTION # 98
Compound authorization groups provide a means to:
Answer: D
Explanation:
Compound authorization groups allow access rights from multiple groups or roles to be combined so that an individual's effective permissions can be configured with greater precision. The user may inherit one set of privileges from an organizational role, another from a functional group, and additional permissions from a specialized operational responsibility. The resulting authorization can therefore be more granular than assigning every privilege individually.
DAMA's Data Security framework separates authentication-establishing identity-from authorization, which determines what an authenticated identity is permitted to access or perform. Group-based authorization is a practical mechanism for implementing those decisions. CDMP-oriented references for this specific item identify "precisely configure an individual's access to a system" as the intended answer.
The principle must nevertheless be governed carefully. Excessive overlapping group memberships can make effective permissions difficult to review and can create segregation-of-duties conflicts. Periodic access reviews, least-privilege design, role ownership, and audit trails are therefore necessary complementary controls.
Encryption addresses confidentiality of data in transit or storage and is unrelated to how permissions are assigned. Audit preparation may benefit from structured authorization, but it is not the principal purpose.
Reference Topics: DAMA-DMBOK2 Chapter 7 - Authorization; Access Control; Least Privilege; Security Roles; Audit and Monitoring.
NEW QUESTION # 99
Mapping requirements and rules for moving data from source to target enables:
Answer: C
Explanation:
Source-to-target mapping enables Transformation. DAMA-DMBOK2 treats mapping as closely synonymous with transformation because a mapping defines how data in one source structure will be converted into the structure, format, representation, or value required by the target.
A mapping specification typically identifies the source attribute, target attribute, extraction conditions, target population rules, intermediate staging transformations, calculations, lookup requirements, and any changes required to make the source data conform to the target representation. DMBOK2 specifically explains that mapping sources to targets involves defining the rules for transforming information from one location and format into another.
Extraction simply retrieves data from the source. Loading places data into the target. Transformation is the activity that applies structural, syntactic, semantic, or value-level modifications between those stages.
The Data Quality connection is substantial. Mappings may standardize dates, convert units, harmonize codes, resolve reference values, remove duplicates, or enforce business rules. If mapping metadata is incomplete or incorrect, the transformation process can introduce rather than correct quality defects.
For this reason, source-to-target mapping should be governed, version-controlled, documented as metadata, traceable through lineage, and validated against agreed business definitions and Data Quality requirements.
Reference Topics: DAMA-DMBOK2 Chapter 8 - Map Data Sources to Targets; Transformation; ETL
/ELT; Metadata Lineage; Chapter 13 - Data Cleansing and Standardization.
NEW QUESTION # 100
Database monitoring tools measure key database metrics, such as:
Answer: D
Explanation:
DAMA-DMBOK2 identifies capacity, availability, cache performance, and user statistics as representative metrics captured by database-monitoring tools. Database monitoring is an operational control mechanism used by Database Administrators and platform teams to understand whether database infrastructure is available, adequately sized, responsive, and being used as expected. DMBOK2 explicitly describes monitoring tools as automating the observation of metrics such as capacity, availability, cache performance, and user statistics.
Capacity metrics indicate resource consumption and future growth requirements. Availability measures whether data services remain accessible when required. Cache-performance measures help identify inefficient access patterns and bottlenecks, while user statistics provide information about workload and database consumption.
The other choices mix design concepts, CRUD operations, or Data Quality concepts with operational monitoring measures. For example, normalization is a modelling technique rather than a routine runtime performance metric. Similarly, "create, read, update, delete" describes basic data operations rather than monitoring indicators.
Although database performance and Data Quality are distinct disciplines, poor operational performance can affect Data Quality dimensions such as timeliness and availability. Monitoring therefore supports the technical environment in which governed, reliable information is delivered to users and applications.
Reference Topics: DAMA-DMBOK2 - Data Storage and Operations; Database Operations; Capacity and Availability Management; Monitoring; Chapter 13 - Timeliness and Operational Fitness for Purpose.
NEW QUESTION # 101
Well executed metadata management enables:
Answer: D
Explanation:
The fundamental outcome of well-executed Metadata Management is a consistent understanding of data resources. DAMA-DMBOK2 states this directly: effective Metadata Management enables consistent understanding of data resources and supports more efficient development across organizational boundaries.
Metadata supplies context that raw data cannot provide by itself. It explains business meaning, technical structures, valid domains, calculations, transformations, ownership, lineage, systems of record, access restrictions, known quality issues, and update schedules. DMBOK2 also identifies Data Quality rules and measurement results as forms of business metadata.
Consequently, Metadata Management is broader than database design, dimensional reporting, authorization, or Data Modeling. Each of those activities consumes metadata, but none represents its complete objective.
This capability is essential to Data Quality. A dataset cannot be meaningfully evaluated for fitness for purpose unless users know what its fields mean, where the data originated, how values were transformed, which business rules apply, and which source is authoritative. Metadata therefore provides the semantic and technical framework required to define and interpret Data Quality measurements consistently.
DAMA's current DMBOK2 revision also explicitly strengthens the relationship between Data Quality and Metadata Management.
Reference Topics: DAMA-DMBOK2 Metadata Management - Goals and Principles; Business Metadata; Technical Metadata; Data Lineage; Chapter 13 - Data Quality and Metadata Management.
NEW QUESTION # 102
The implementation of a 'Super Type - Sub Type' structure can use the following 2 options:
Answer: C
Explanation:
DAMA-DMBOK2 identifies two recognized approaches for resolving logical supertype-subtype abstractions when moving into physical database design: Subtype Absorption and Supertype Partition.
With Subtype Absorption, attributes belonging to subtype entities are incorporated into the table representing the supertype. Attributes that apply only to particular subtypes may consequently be nullable. This approach reduces the number of physical tables but can introduce sparsity and requires explicit rules to ensure subtype- specific attributes remain semantically valid.
With Supertype Partition, the attributes belonging to the supertype are carried into separate physical tables representing each subtype. This can simplify subtype-specific processing but introduces duplication of common structures and requires disciplined metadata and modelling control.
The choice has direct consequences for Data Quality. Subtype absorption requires validity and completeness rules to distinguish legitimate nulls from missing data. Supertype partition requires consistency controls to ensure shared attributes retain identical definitions and constraints across subtype tables. Metadata repositories should document subtype discriminators, attribute definitions, constraints, and inheritance rules.
DMBOK2 therefore treats the transformation as a deliberate physical modelling decision rather than the generic "merge/split" terminology presented in the distractors.
Reference Topics: DAMA-DMBOK2 Chapter 5 - Physical Data Modeling; Resolve Logical Abstractions; Chapter 13 - Validity, Completeness and Consistency; Metadata Management.
NEW QUESTION # 103
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