Download DMF-1220 Data Management Fundamentals PDF To Be The Best Preparation Materials

Eleanor2024-10-14T15:24:53+00:00

By Eleanor CDMP Certification, DAMA

DMF-1220 Data Management Fundamentals certification exam is required for all CDMP certification levels: Associate, Practitioner and Master. So there are many IT students who want to be certified professionals by passing the DMF-1220 exam. When preparing for the Data Management Fundamentals DMF-1220 exam, you can download the DMF-1220 pdf file from ITPrepare to be the best preparation materials. The professional team has collected 485 exam questions and answers to help you learn well, and finally, you can pass your DMF-1220 Data Management Fundamentals certification exam successfully.

Data Management Fundamentals DMF-1220 Free Dumps Demo Below

Page 1 of 10

1. Most document programs have policies related to:

Scope and compliance audits

Proper destruction of records

Proper construction of records

Identification and protection of vital records

Partition tolerance

All of the above

2. The deliverables in the data architecture context diagram include:

Data flows

Enterprise data

Implementation roadmap

Data Value Chains

None of the above

All of the above

3. A hacker is a person who finds unknown operations and pathways within complex computer system. Hackers are only bad.

TRUE

FALSE

4. Please select correct term for the following sentence: An organization shall assign a senior executive to appropriate individuals, adopt policies and processes to guide staff and ensure program audibility.

Principle of integrity

Principle of availability

Principle of retention

Principle of accountability

5. Inputs in the data quality context diagram include:

Data stores

Data lakes

Business requirements

Data quality expectations

6. What model is the highest level model within the enterprise data model?

Logical model

Physical model

Conceptual model

Subject Area model

7. Please select valid modelling schemes or notations

NoSQL

Dimensional

Relational

Object-orientated

Fact-based

Matrix-based

8. Within the Data Handling Ethics Context Diagram a key deliverable is the Ethical Data Handling Strategy.

TRUE

FALSE

9. Improving an organization’s ethical behaviour requires an informal Organizational Change Management (OCM) process.

TRUE

FALSE

10. Big data primarily refers specifically to the volume of the data.

TRUE

FALSE

Page 2 of 10

11. The business glossary should capture business terms attributes such as:

Lineage

Utilization

Common misunderstanding in terms

System development

Algorithms to supporting definitions

All of the above

12. A limitation of the centralized metadata repository approach is it may be less expensive.

TRUE

FALSE

13. Please select the transition phases in Bridges’ Transition process:

The neutral zone

The new beginning

The ending

The transition

The translation

The game

14. A ‘Golden Record’ means that it is always a 100% complete and accurate representation of all entities within the organization.

TRUE

FALSE

15. Vulnerability is defined as:

a patch in a system that allows it to be successfully unpatched and compromised.

being highly data risk rated

a strength in a system that allows external stakeholders to view data records.

a weakness or defect in a system that allows it to be successfully attacked and compromised.

16. Valuation information, as an example of data enrichment, is for asset valuation, inventory and sale.

TRUE

FALSE

17. 1.Improving data quality requires a strategy that accounts for the work that needs to be done and the way people will execute it.

TRUE

FALSE

18. Please select the answers that correctly describes where the costs of poor quality data comes from.

Scrap and rework

Organizational conflict

High job satisfaction

High productivity

Reputational costs

Compliance costs

19. Examples of technical metadata include:

Column Properties

Access permissions

Internal

Conceptual

ETL job details

20. The implementation of a Data Warehouse should follow guiding principles, including:

Data Efficiency

One size does not fit all

Contracts

Focus on the business goals

Start with the end in mind

Collaborate

Page 3 of 10

21. Information gaps represent enterprise liabilities with potentially profound impacts on operational effectiveness and profitability.

TRUE

FALSE

22. Self-service is a fundamental delivery channel in the BI portfolio.

TRUE

FALSE

23. Please select the correct General Accepted Information Principles:

Asset Principle

Audit Principle

Due Diligence Principle

Going Concern Principle

Ethical Principle

All of the above

24. Data and text mining use a range of techniques, including:

Profiling

Application reduction

Association

Data reduction

Clustering

All of the above

25. An input in the Metadata management context diagram does not include:

Business requirements

Business metadata

Technical metadata

Metadata standards

Process Metadata

26. The Shewhart chart contains the following elements:

Plan

Utilization

Check

Act

All of the above

27. Veracity refers to how difficult the data is to use or to integrate.

TRUE

FALSE

28. Data profiling examples include:

Counts of null

Max/Min value

Max/Min length

Frequency distribution

Data type and format

None of the above

29. Architects seek to design in a way that brings value to an organisation. To reach these goals, data architects define and maintain specifications that:

Align data architecture with enterprise strategy and business architecture

Provide a standard business vocabulary for data and components

Outline high-level integrated designs to meet these requirements.

Integrate with overall enterprise architecture roadmap

Define the current state of data in the organization.

Express strategic data requirements

30. The four main types of NoSQL databases are:

Document

Row-orientated

Graph

Strategic

Key-value

Column-orientated

Page 4 of 10

31. DBAs and database architects combine their knowledge of available tools with the business requirements in order to suggest the best possible application of technology to meet organizational goals.

FALSE

TRUE

32. RACI is an acronym that is made up of the following terms.

Control

Responsible

Accountable

Informed

Reliable

Consulted

33. A goal of a Reference and Master Data Management program include enabling master and reference data to be shared across enterprise functions and applications.

FALSE

TRUE

34. SSD is the abbreviation for Solid State Dimension.

TRUE

FALSE

35. A data dictionary is necessary to support the use of a DW.

TRUE

FALSE

36. The accuracy dimension has to do with the precision of data values.

TRUE

FALSE

37. Temporal aspects usually include:

Valid time

Transmitting time

Transaction time

Value time

38. Data Warehouse describes the operational extract, cleansing, transformation, control and load processes that maintain the data in a data warehouse.

FALSE

TRUE

39. Location Master Data includes business party addresses and business party location, as well as facility addresses for locations owned by organizations.

TRUE

FALSE

40. Confirming and documenting understanding of different perspectives facilitate:

Formalization

Normalization

Scope definition

Knowledge retention/documentation

Page 5 of 10

41. SLA Stands for:

Service Level Agreement

System Lifecycle Audit

System Latitude Audit

Service Lifecycle Audit

42. As an often-overlooked aspects of basic data movement architecture, Process controls include:

Consistency logging

Exception logs

Database activity logs

Alerts

Exception logs

All of the above

43. Business people must be fully engaged in order to realize benefits from the advanced analytics.

TRUE

FALSE

44. Small reference data value sets in the logical data model can be implemented in a physical model in three common ways:

Create a matching separate code table

Create a master shared code table

None of the above

Program integration by joining tables

Embed rules or valid codes into the appropriate object’s definition.

Roadmap Development

45. Data Integration and Interoperability (DII) describes processes related to the movement and consolidation of data within and between data stores, applications and organizations.

TRUE

FALSE

46. Elements that point to differences between warehouses and operational systems include:

Historical

Data security standards

Subject-orientated

Non-volatile

Data quality

Integrated

47. Reference and Master Data Management follow these guiding principles:

Quality

Stewardship

Authority

Ownership

Exclusivity

Inclusivity

48. Orchestration is the term used to describe how multiple processes are organized and executed in a system.

TRUE

FALSE

49. Match rules for different scenarios require different workflows, including:

Consistency rules

Duplicate identification match rules

Match-merge rules

Match-split rules

Match-link rules

All of the above

50. Master data management includes several basic steps, which include: Develop rules for accurately matching and merging entity instances.

TRUE

FALSE

Page 6 of 10

51. When constructing an organization’s operating model cultural factors must be taken into consideration.

TRUE

FALSE

52. CMA is an abbreviation for Capability Maturity Assessment.

TRUE

FALSE

53. Functionality-focused requirements associated with a comprehensive metadata solution, include:

Synchronization

Structure

None of the above

History

Volatility

Access rights

54. Key processing steps for MDM include:

Data model management

Data acquisition

Data validation, standardization and enrichment

Entity resolution

Data sharing and stewardship

None of the above

55. The database administrator (DBA) is the most established and the most widely adopted data professional role.

TRUE

FALSE

56. A business driver for Master Data Management program is managing data quality.

TRUE

FALSE

57. The Belmont principles that may be adapted for Information Management disciplines, include:

Respect for Persons

Respect for Machines

Beneficence

Criminality

Justice

58. A dimensional physical data model is usually a star schema, meaning there is one structure for each dimension.

FALSE

TRUE

59. The warehouse has a set of storage areas, including:

Staging area

Data marts

Consistent object attributes

Operational data store (ODS)

Cubes

Central warehouse

60. While the focus of data quality improvement efforts is often on the prevention of errors, data quality can also be improved through some forms of data processing.

TRUE

FALSE

Page 7 of 10

61. A synonym for transformation in ETL is mapping. Mapping is the process of developing the lookup matrix from source to target structures, but not the result of the process.

TRUE

FALSE

62. The IT security policy provides categories for individual application, database roles, user groups and information sensitivity.

TRUE

FALSE

63. The Data Warehouse (DW) is a combination of three primary components: An integrated decision support database, related software programs and business intelligence reports.

FALSE

TRUE

64. The primary goal of data management capability assessment is to evaluate the current state of critical data management activities in order to plan for improvement.

TRUE

FALSE

65. Sample value metrics for a data governance program include:

Reduction of risk

Improved efficiency in operations

Effectiveness of education

Achievements of goals and objectives

Contributions to business objectives

Effectiveness of communication

66. Some ways to measure value of data include:

Replacement cost

Market value

Selling Data

Risk cost

Identified opportunities

All of the above

67. Data quality management is a key capability of a data management practice and organization.

TRUE

FALSE

68. Advantages if a centralized metadata repository include:

Quick metadata retrieval

None of the above

Combining data from multiple other tables in advance to avoid costly run-time joins

All of the above

High availability

Low latency, since it is independent of the source systems

69. A deliverable in the data architecture context diagram includes an implementation roadmap.

TRUE

FALSE

70. The data warehouse and marts differ from that in applications as the data is organized by subject rather than function.

TRUE

FALSE

Page 8 of 10

71. The operational data quality management procedures depend on the ability to measure and monitor the applicability of data.

FALSE

TRUE

72. 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.

TRUE

FALSE

73. Some document management systems have a module that may support different types of workflows such as:

Quality Assurance Testing (QA)

Manual workflows that indicate where the user send the document

User Acceptance Testing (UAT)

Dynamic rules that allow for different workflows based on content

All of the above

None of the above

74. Examples of concepts that can be standardized within the data architecture knowledge area include:

Enterprise data models

System naming conventions

None of the above

Data security standards

Data quality rules

Tool standards

75. BI tool types include:

BPM

Operational reporting

Data lake extraction

Diagnostic, self-service analytics

Descriptive, self-service analytics

Reduction of risk

76. A data governance strategy defines the scope and approach to governance efforts. Deliverables include:

Charter

Operating framework and accountabilities

Implementation roadmap

Plan for operational success

All of the above

None of the above

77. With reliable Metadata an organization does not know what data it has, what the data represents and how it moves through the systems, who has access to it, or what it means for the data to be of high quality.

TRUE

FALSE

78. The first two steps in the data science process are:

Define Big Data data strategy & Business Need(s)

Choose Data Sources

Develop Data Science Hypotheses and Mehods

Acquire & Ingest Data source(s)

79. Consistent input data reduces the chance of errors in associating records. Preparation processes include:

Standardization

Enrichment CValidation

Database management

80. All metadata management solutions include architectural layers including:

Metadata Quality Assurance Testing

Metadata integration

Metadata usage

Metadata delivery

Metadata control and management

None of the above

Page 9 of 10

81. The implementation of a Data Warehouse should follow these guiding principles:

Managing performance of data assets

Focus on the business goals

Managing the availability of data throughout the data lifecycle

Start with the end in mind

Managing the performance of data transactions

Collaborate

82. DAMA International’s Certified Data Management Professional (CDMP) certification required that data management professionals subscribe to a formal code of ethics, including an obligation to handle data ethically for the sake of society beyond the organization that employs them.

TRUE

FALSE

83. Data lineage is useful to the development of the data governance strategy.

TRUE

FALSE

84. The most common drivers for initiating a Mater Data Management Program are:

Metadata insecurity

Managing data quality

Reducing risk

Managing the costs of data integration

Meeting organizational data requirements

Reducing latency

85. Data stewardship is the least common label to describe accountability and responsibility for data and processes to ensure effective control and use of data assets.

TRUE

FALSE

86. Data quality issues cannot emerge at any point in the data lifecycle.

FALSE

TRUE

87. A Data Management Maturity Assessment (DMMA) can be used to evaluate data management overall, or it can be used to focus on a single Knowledge Area or even a single process.

TRUE

FALSE

88. Three data governance operating models types include:

Centralized

Decentralized

Feathered

Federated

Replicated

Duplicated

89. Business metadata focuses largely on the content and condition of the data and includes details related to data governance.

TRUE

FALSE

90. Use business rules to support Data Integration and Interoperability at various points, to:

Direct the flow of data in the organization

Monitor the organization’s operational data

Software alerts when events are triggered

Direct when to automatically trigger events and alerts

Consistency in allocation event resources

None of the above

Page 10 of 10

91. Many people assume that most data quality issues are caused by data entry errors. A more sophisticated understanding recognizes that gaps in or execution of business and technical processes cause many more problems that mis-keying.

TRUE

FALSE

92. The goals of Data Integration and Interoperability include:

Provide data securely, with regulatory compliance, in the format and timeframe needed.

Lower cost and complexity of managing solutions by developing shared models and interfaces.

Managing the availability of data throughout the data lifecycle

Provide the starting point for customizations, integration or even replacement of an application

Identify meaningful events and automatically trigger alerts and actions.

Support business intelligence, analytics, master data management and operational efficiency efforts.

93. Please select the correct general cost and benefit categories that can be applied consistently within an organization.

Cost of erasing data from servers

Cost of improving data

What the data could be sold for

Benefit of higher quality data

Cost of replacing data if it were lost

What competitors would pay for data

94. Data professionals involved in Business Intelligence, analytics and Data Science are often responsible for data that describes: who people are; what people do; where people live; and how people are treated. The data can be misused and counteract the principles underlying data ethics.

FALSE

TRUE

95. Corporate Information Factory (CIF) components include:

Data marts

Objectives

Operational Reports

Reduction of risk

Staging Area

Contributions to business objectives

96. Different types of product Master Data solutions include:

People Lifecycle Product Management (PLPM)

Product data in Manufacturing Execution Systems (MES)

None of the above

Product Data in Enterprise Resource Planning (ERP)

Product Lifecycle Management (PLM)

97. Data professional should not balance the short-term versus long-term business interests.

TRUE

FALSE

98. There are several reasons to denormalize data. The first is to improve performance by:

Creating smaller copies of fata to reduce costly run-time calculations and/or table scans of large tables.

None of the above

Pre-calculating and sorting costly data calculations to avoid runt-time system resource competition.

Making tables more readable when no foreign key exists

Combining data from multiple other tables in advance to avoid costly run-time joins

All of the above

99. Data architect: A senior analyst responsible for data architecture and data integration.

TRUE

FALSE

100. Data security issues, breaches and unwarranted restrictions on employee access to data cannot directly impact operational success.

TRUE

FALSE

Download DMF-1220 Data Management Fundamentals PDF To Be The Best Preparation Materials

Download DMF-1220 Data Management Fundamentals PDF To Be The Best Preparation Materials

Data Management Fundamentals DMF-1220 Free Dumps Demo Below

Share this post

Author