DMF-1220絶対合格、DMF-1220日本語認定対策

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試験の概要は毎年新しいポリシーに基づいて変更され、DMF-1220質問トレントおよびその他の教育用ソフトウェアは、新しい試験の概要の後、シラバスおよび理論と実践の最新の開発および改訂に従って変更されます対応する変更は、アウトラインに非常に同意します。 DMF-1220試験問題は、教材の完全なセットの完璧な形です。教育概要は、カバーされているすべての知識ポイントの概要を網羅し、DMF-1220候補者のデッドアングルは、毎年の提案範囲と傾向を示します。

DAMA DMF-1220 Exam Syllabus Topics:

Section	Objectives
Data Management Fundamentals	- Data Risk Management 1. Risk identification and assessment 2. Data protection and compliance 3. Risk mitigation strategies - Data Business and Data Management 1. Business drivers and data roles 2. Data management frameworks 3. Data management definitions and principles - Data Quality 1. Data quality dimensions 2. Data quality measurement and improvement 3. Data profiling and cleansing - Data Modeling and Design 1. Normalization and denormalization 2. Data modeling techniques 3. Conceptual, logical, and physical models - Data Governance 1. Governance framework and scope 2. Policies, standards, and procedures 3. Data quality management 4. Governance bodies and organizational roles - Data Storage and Operations 1. Data storage design principles 2. Database security and access control 3. Database architecture and technology - Metadata Management 1. Metadata architecture and repository 2. Metadata types and definitions 3. Metadata standards and integration - Content and Document Management 1. Records management 2. Unstructured data management 3. Content lifecycle management - Data Strategy 1. Strategic planning process 2. Data management strategy development 3. Data governance strategy - Data Integration and Interoperability 1. Data integration techniques 2. ETL/ELT processes 3. Data interoperability standards - Data Security 1. Privacy regulations compliance 2. Security frameworks and controls 3. Data classification and handling - Master Data and Reference Data 1. MDM and RDM concepts 2. Master data management processes 3. Reference data management - Data Architecture 1. Data modeling and design 2. Architecture frameworks and tools 3. Enterprise data architecture concepts - Data Ethics 1. Ethical principles and practices 2. Responsible AI and algorithmic bias 3. Privacy and consumer protection - Data Warehousing and Business Intelligence 1. Data warehousing concepts and architectures 2. Reporting and visualization 3. BI and analytics fundamentals

Section

Objectives

Data Management Fundamentals

- Data Risk Management

1. Risk identification and assessment
2. Data protection and compliance
3. Risk mitigation strategies

- Data Business and Data Management

1. Business drivers and data roles
2. Data management frameworks
3. Data management definitions and principles

- Data Quality

1. Data quality dimensions
2. Data quality measurement and improvement
3. Data profiling and cleansing

- Data Modeling and Design

1. Normalization and denormalization
2. Data modeling techniques
3. Conceptual, logical, and physical models

- Data Governance

1. Governance framework and scope
2. Policies, standards, and procedures
3. Data quality management
4. Governance bodies and organizational roles

- Data Storage and Operations

1. Data storage design principles
2. Database security and access control
3. Database architecture and technology

- Metadata Management

1. Metadata architecture and repository
2. Metadata types and definitions
3. Metadata standards and integration

- Content and Document Management

1. Records management
2. Unstructured data management
3. Content lifecycle management

- Data Strategy

1. Strategic planning process
2. Data management strategy development
3. Data governance strategy

- Data Integration and Interoperability

1. Data integration techniques
2. ETL/ELT processes
3. Data interoperability standards

- Data Security

1. Privacy regulations compliance
2. Security frameworks and controls
3. Data classification and handling

- Master Data and Reference Data

1. MDM and RDM concepts
2. Master data management processes
3. Reference data management

- Data Architecture

1. Data modeling and design
2. Architecture frameworks and tools
3. Enterprise data architecture concepts

- Data Ethics

1. Ethical principles and practices
2. Responsible AI and algorithmic bias
3. Privacy and consumer protection

- Data Warehousing and Business Intelligence

1. Data warehousing concepts and architectures
2. Reporting and visualization
3. BI and analytics fundamentals

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DAMA Data Management Fundamentals 認定 DMF-1220 試験問題 (Q68-Q73):

質問 # 68
A completely distributed architecture maintains a single access point. The metadata retrieval engine responds to user requests by retrieving data from source systems in real time.

A. TRUE
B. FALSE

正解：A

質問 # 69
An organization has a legitimate interest in commercializing data. So why is the economic value of data a core concept of data handling ethics?

A. An organization does not own its customer data so it is illegal to monetize the data
B. In owning data, we have a commitment to society to determine how value can be accessed and who can derive this value
C. Organizations dependent on information to provide product and services that meet Customer and Community needs, must ensure the ethical handling of customer information
D. Data is used in every business process. The effectiveness of the process is dependent on the quality of the data it is dependent on
E. It is critical that customers are not exploited in ways that are detrimental to the customer in the long run

正解：C

質問 # 70
Deliverables in the document and content management context diagram include:

A. Policy and procedure
B. Metadata and reference data
C. Content and records management strategy
D. Data governance
E. Data storage and operations
F. Audit trail and log

正解：A、C、D、F

質問 # 71
An organization can enhance its Data Governance program and thereby improve its approach to enterprise data management. This is important for the following reason:

A. Since most organizations start Data Governance in silos and not at the enterprise level, it is important to be able to assess current state and determine desired targeted state that supports the overall vision
B. Assessing current state for Data Governance may not tell the organization the current state of other components
C. Data Governance programs are rarely enhanced once they are launched
D. Often organizations have a data repository or dictionary that is just as good as Data Governance
E. Starting a Data Governance program does not require that the organization start a data management program

正解：A

質問 # 72
Gathering and interpreting results from a DMM or Data Governance assessment are important because:

A. Data Management and Data Governance results are gathered and interpreted differently, depending on the organization being evaluated
B. Interpreting these results is difficult
C. Lack of results gathered can indicate a lack of assessment performance
D. Performing these activities objectively can ensure the success of the assessment and affect the acceptance and implementation of the data management and/or Data Governance program
E. The assessments are done by different teams within the organization and may have different results

正解：D

質問 # 73
......

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