QUESTION: 37

MDM is a lifecycle management process that includes the following activities with the exception of which activity?

Provisioning of access to trusted data across applications, either through direct reads, data services, or by replication feeds to transactional, warehousing or analytical data stores
Identifying multiple instances of the same entity represented within and across data sources:
building and maintaining identifiers and cross-references to enable information integration
Ensuring effective and efficient retrieval and use of data and information by ETL logic
Enforcing the use of Master Data values within the organization
Identifying improperly matched or merged instances and ensuring they are resolved and correctly associated with identifiers

Answer(s): C

Explanation:

MDM (Master Data Management) is a lifecycle management process that includes various activities to ensure the quality, consistency, and accessibility of master data across an organization. These activities include:
Provisioning of Access: Ensuring that trusted master data is accessible across applications through various methods such as direct reads, data services, or replication feeds. Identifying Multiple Instances: Detecting and managing multiple representations of the same entity within and across data sources. This involves creating and maintaining identifiers and cross-

references for integration.
Enforcing Use of Master Data: Ensuring that the organization consistently uses master data values in processes and applications.
Resolving Improper Matches: Identifying and resolving improperly matched or merged data instances to maintain data integrity.
The activity of "Ensuring effective and efficient retrieval and use of data and information by ETL logic" (C) is not specific to MDM.
While ETL (Extract, Transform, Load) processes are crucial for data integration and warehousing, they are not a core activity unique to the MDM lifecycle.

Reference:

DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition. "Master Data Management and Data Governance" by Alex Berson and Larry Dubov.

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QUESTION: 38

What is the critical need of any Reference & Master Data effort?

Funding
Metadata
Project Management
Executive Sponsorship
ETL toolset

Answer(s): D

Explanation:

The critical need of any Reference & Master Data effort is executive sponsorship. Executive sponsorship provides the necessary authority, visibility, and support for the MDM initiative. Key aspects include:
Strategic Alignment: Ensures that the MDM effort aligns with the organization's strategic goals and objectives.
Resource Allocation: Secures the required funding, personnel, and other resources needed for the MDM program.
Stakeholder Engagement: Facilitates engagement and commitment from key stakeholders across the organization.
Governance and Oversight: Provides governance and oversight to ensure the MDM program adheres to best practices and delivers value.
Without executive sponsorship, MDM initiatives often struggle to gain traction, secure necessary resources, and achieve long-term success.

Reference:

DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition. "Master Data Management and Data Governance" by Alex Berson and Larry Dubov.

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QUESTION: 39

What activity is helpful in mapping source system data for MDM efforts?

Data profiling
ETL toolset
Process modeling
Data dictionary
Data modeling

Answer(s): A

Explanation:

Data profiling is a crucial activity in mapping source system data for MDM efforts. Data profiling involves analyzing data from source systems to understand its structure, content, and quality. Key steps include:
Data Assessment: Evaluating the data to identify patterns, inconsistencies, and anomalies.

Data Quality Analysis: Measuring the quality of data in terms of accuracy, completeness, consistency, and uniqueness.
Metadata Extraction: Extracting metadata to understand data definitions, formats, and relationships. Data Cleansing: Identifying and correcting data quality issues to ensure that the data is suitable for integration into the MDM system.
By performing data profiling, organizations can gain insights into the current state of their data, identify potential issues, and develop strategies for data integration and quality improvement.

Reference:

DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition. "Data Quality: The Accuracy Dimension" by Jack E. Olson.

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QUESTION: 40

A 'Curation Zone' is a data architecture component used to:

Perform advanced analytic
Ingest raw source system data
Validate source system content
Share reference data
Semantically formalize source system content

Answer(s): E

Explanation:

A 'Curation Zone' is a data architecture component used to semantically formalize source system content. This involves:
Data Curation: The process of organizing, integrating, and enriching raw data to make it meaningful and useful.
Semantic Formalization: Applying semantic models, ontologies, and metadata to standardize and contextualize the data.
Data Quality Enhancement: Ensuring the data meets quality standards through cleansing and validation processes.
Metadata Management: Capturing and managing metadata to provide context and meaning to the data.
The curation zone plays a critical role in transforming raw data into high-quality, semantically enriched information that can be effectively used for analysis, decision-making, and operational processes.

Reference:

DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition. "Data Governance: How to Design, Deploy, and Sustain an Effective Data Governance Program" by John Ladley.

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