DMF-1220 Exam Cram Questions - DMF-1220 New Study Notes

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| Section | Objectives |
|---|
| Topic 1: Data Management Fundamentals | - Data Governance
- 1. Data quality management
- 2. Governance bodies and organizational roles
- 3. Governance framework and scope
- 4. Policies, standards, and procedures
- Data Integration and Interoperability
- 1. Data integration techniques
- 2. Data interoperability standards
- 3. ETL/ELT processes
- Master Data and Reference Data
- 1. MDM and RDM concepts
- 2. Reference data management
- 3. Master data management processes
- Content and Document Management
- 1. Records management
- 2. Content lifecycle management
- 3. Unstructured data management
- Data Storage and Operations
- 1. Database security and access control
- 2. Database architecture and technology
- 3. Data storage design principles
- Data Risk Management
- 1. Risk mitigation strategies
- 2. Data protection and compliance
- 3. Risk identification and assessment
- Data Ethics
- 1. Responsible AI and algorithmic bias
- 2. Privacy and consumer protection
- 3. Ethical principles and practices
- Data Strategy
- 1. Data management strategy development
- 2. Data governance strategy
- 3. Strategic planning process
- Data Architecture
- 1. Architecture frameworks and tools
- 2. Data modeling and design
- 3. Enterprise data architecture concepts
- Data Modeling and Design
- 1. Data modeling techniques
- 2. Normalization and denormalization
- 3. Conceptual, logical, and physical models
- Data Warehousing and Business Intelligence
- 1. BI and analytics fundamentals
- 2. Reporting and visualization
- 3. Data warehousing concepts and architectures
- Data Business and Data Management
- 1. Data management frameworks
- 2. Data management definitions and principles
- 3. Business drivers and data roles
- Data Security
- 1. Security frameworks and controls
- 2. Data classification and handling
- 3. Privacy regulations compliance
- Data Quality
- 1. Data quality dimensions
- 2. Data profiling and cleansing
- 3. Data quality measurement and improvement
- Metadata Management
- 1. Metadata types and definitions
- 2. Metadata architecture and repository
- 3. Metadata standards and integration
|
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DAMA Data Management Fundamentals Sample Questions (Q191-Q196):
NEW QUESTION # 191
The advantage of a decentralised Data Governance model over a centralised model is:
- A. The easier implementation of industry data models
- B. An increased level of ownership from local decision making groups
- C. The common metadata repository configurations
- D. The cheaper execution of Data Governance operations
- E. Having a common approach to resolving Data Governance issues
Answer: B
NEW QUESTION # 192
According to the DMBoK, Data Governance is central to Data Management. In practical terms, what other functions of Data Management are required to ensure that your Data Governance programme is successful?
- A. Data Architecture, Data Security, and Metadata Management
- B. Master Data, Data Quality, and Data Development
- C. Data Modelling, Data Architecture and Master Data Management
- D. Data Quality, Data Architecture, and Metadata Management
- E. Data Quality, Content and Document Management, and Data Development
Answer: D
NEW QUESTION # 193
Differentiating between data and information. Please select the correct answers based on the sentence below:
Here is a marketing report for the last month [1]. It is based on data from our data warehouse [2]. Next month these results [3] will be used to generate our month-over-month performance measure [4].
- A. [1] Information, [2] Information, [3] Data, [4] Information
- B. [1] Information, [2] Data, [3] Data, [4] Information
- C. [1] Data, [2] Data, [3] Data, [4] Information
- D. [1] Data, [2] Information, [3] Data, [4] Data
Answer: B
NEW QUESTION # 194
CMDB provide the capability to manage and maintain Metdata specifically related to the IT assets, the relationships among them, and contractual details of the assets.
Answer: B
NEW QUESTION # 195
What changes in data processing when a gas utility checks the condition of its assets using a drone rather than a team of people driving alongside the pipeline?
- A. Previously, they received reference data, now they receive incident data to find the work order
- B. Video data is much easier to read than structured data from the teams
- C. Previously, they received condition data, now they receive observation data that needs to be analyzed to extract the condition
- D. Previously, they received metadata, now they receive transactional data that needs to be analyzed to extract the data
- E. Previously, they received master data, now they receive reference data to find the incidents
Answer: C
Explanation:
Switching from human inspection to drones changes the data from direct condition reports to raw observation data (e.g., images or sensor readings) that requires analysis to derive asset conditions. The DAMA-DMBOK notes: "The introduction of new data collection methods, such as IoT or drones, often shifts data processing from structured, interpreted data to raw, observational data that requires additional processing to extract meaningful insights" (DMBOK2, Chapter 13: Data Integration and Interoperability, p. 517).
Options B, D, and E misalign data types (e.g., reference, master, or metadata are not relevant here), and C is incorrect as video data is typically harder to process than structured data.
Reference:DAMA-DMBOK2, Chapter 13: Data Integration and Interoperability, Section 4.1, p. 517.
NEW QUESTION # 196
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