DMF-1220 dumps materials - exam dumps for DMF-1220: Data Management Fundamentals

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DAMA DMF-1220 Exam Syllabus Topics:

Section	Objectives
Topic 1: Data Storage and Operations
Topic 2: Data Security
Topic 3: Data Modeling and Design
Topic 4: Data Architecture
Topic 5: Document and Content Management
Topic 6: Data Governance
Topic 7: Data Quality Management
Topic 8: Reference and Master Data
Topic 9: Data Integration and Interoperability
Topic 10: Metadata Management
Topic 11: Data Warehousing and Business Intelligence

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DAMA Data Management Fundamentals Sample Questions (Q155-Q160):

NEW QUESTION # 155
Key processing steps for MDM include:

A. Data sharing and stewardship
B. Data model management
C. Data validation, standardization and enrichment
D. None of the above
E. Entity resolution
F. Data acquisition

Answer: A,B,C,E,F

NEW QUESTION # 156
The most common drivers for initiating a Mater Data Management Program are:

A. Managing the costs of data integration
B. Metadata insecurity
C. Reducing risk
D. Managing data quality
E. Meeting organizational data requirements
F. Reducing latency

Answer: A,C,D,E

NEW QUESTION # 157
Modeling Bid data is a non-technical challenge but critical if an organization that want to describe and govern its data.

A. TRUE
B. FALSE

Answer: B

NEW QUESTION # 158
The purpose for adding redundancy to a data model (denormalisation) is to:

A. Avoid the loss of data by storing key values more than once
B. Fully utilize all the indexes
C. Improve aggregate database performance across read requests
D. Ensure surrogate keys are retaining their unique values in all satellite tables
E. Make it easier for developers to join tables

Answer: C

NEW QUESTION # 159
A data lake and a data warehouse are the same concepts in so far as:

A. They are not related in any way
B. They are both concerned with using different forms of data governance
C. They are both concerned with producing star schemas and dimensional data models
D. They are both concerned with preparing data for reporting and analytics
E. They are both concerned with duplicating all the operational data

Answer: D

Explanation:
A data lake and a data warehouse differ significantly in architecture, structure, processing, and governance, but both ultimately support the broader objective of making enterprise data available for reporting, analytics, and decision-making . A data warehouse typically contains integrated, cleansed, structured, and purpose- oriented data optimized for BI workloads. A data lake generally stores larger volumes of structured, semi- structured, and unstructured data, often retaining information closer to its original form for later processing and analysis. They therefore do not necessarily duplicate all operational data, and data lakes are not inherently based on dimensional or star-schema models. Both also require appropriate governance rather than fundamentally different forms of governance. Option B captures their principal common business purpose.
DAMA recognizes Data Warehousing/BI and modern data platforms as components of the broader analytical data-management landscape.

NEW QUESTION # 160
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