CDMP Data Management Fundamentals Practice Quiz
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Please select the correct definition of Data Management from the options below.

  • Data Management is the development, execution and supervision of plans, policies, programs and practices that deliver, control, protect and enhance the value of data assets throughout their lifecycles.
  • Data Management is the development, execution and supervision of plans, policies, programs and practices that deliver, control, protect and enhance the value of data and information assets throughout their lifecycles. (correct)
  • Data Management is the development, execution and supervision of plans, policies, programs and practices that deliver, control, protect and enhance the value of information assets throughout their lifecycles.
  • Data Management is the strict control of all plans, policies, programs and practices that enable the business strategy to be successfully executed.

    • The CAP theorem states that at most two of the three properties: consistency, availability, and partition tolerance can exist in any shared data system.

  • True (correct)
  • False

    • SSD is the abbreviation for Solid State Dimension.

  • False (correct)
  • True

    • A sandbox environment can either be a sub-set of the production system, walled off from production processing, or a completely separate environment.

    <p>True</p> Signup and view all the answers

    A sandbox is an alternate environment that allows write-only connections to production data and can be managed by the administrator.

    <p>False</p> Signup and view all the answers

    Data security includes the planning, development, and execution of security policies and procedures to provide authentication, authorization, access, and auditing of data and information assets.

    <p>True</p> Signup and view all the answers

    A deliverable in the data security context diagram is the data security architecture.

    <p>True</p> Signup and view all the answers

    E-discovery is the process of finding electronic records that might serve as evidence in a legal action.

    <p>True</p> Signup and view all the answers

    Content management includes the systems for organizing information resources so that they can specially be stored.

    <p>False</p> Signup and view all the answers

    Content refers to the data and information inside a file, document or website.

    <p>True</p> Signup and view all the answers

    Enterprise content management includes the abbreviation ECM.

    <p>Enterprise content management</p> Signup and view all the answers

    Content needs to be modular, structured, reusable and device and platform independent.

    <p>True</p> Signup and view all the answers

    A controlled vocabulary is a defined list of explicitly allowed terms used to index, categorize, tag, sort and retrieve content through browsing and searching.

    <p>True</p> Signup and view all the answers

    Which ISO standard describes the structure and organization of data quality management?

    <p>ISO 8000</p> Signup and view all the answers

    The accuracy dimension of data quality relates to the precision of data values.

    <p>True</p> Signup and view all the answers

    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.

    <p>True</p> Signup and view all the answers

    When selecting a DMM framework one should consider if it is repeatable.

    <p>True</p> Signup and view all the answers

    The IBM Data Governance Council model is organized around four key categories. Select the answer that is not a category.

    <p>System Lifecycles</p> Signup and view all the answers

    A communication plan includes an engagement model for stakeholders, the type of information to be shared, and the schedule for sharing information.

    <p>True</p> Signup and view all the answers

    Oversight for the DMMA process belongs to the Data Quality team.

    <p>False</p> Signup and view all the answers

    DMMA ratings represent a snapshot of the organization’s capability level.

    <p>True</p> Signup and view all the answers

    A critical step in data management organization design is identifying the best-fit operating model for the organization.

    <p>True</p> Signup and view all the answers

    Decentralized informality can be made more formal through a documented series of connections and accountabilities via a RACI matrix.

    <p>True</p> Signup and view all the answers

    Factors that have shown to play a key role in the success of effective data management organizations does not include:

    <p>IT sponsorship</p> Signup and view all the answers

    Communication should start later in the process as too many inputs will distort the vision.

    <p>False</p> Signup and view all the answers

    Data governance and IT governance are the same thing.

    <p>False</p> Signup and view all the answers

    Governance ensures data is managed, but does not include the actual act of managing data.

    <p>True</p> Signup and view all the answers

    The Data Governance Council (DGC) manages data governance initiatives, issues, and escalations.

    <p>True</p> Signup and view all the answers

    Data Governance Office (DGO) focuses on enterprise-level data definitions and data management standards across all DAMA-DMBOK knowledge areas. Consists of coordinating data management roles.

    <p>True</p> Signup and view all the answers

    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.

    <p>False</p> Signup and view all the answers

    Data asset valuation is the process of understanding and calculating the economic value of data to an organization. Value comes when the economic benefit of using data outweighs the costs of acquiring and storing it.

    <p>True</p> Signup and view all the answers

    Data governance program must contribute to the organization by identifying and delivering on specific benefits.

    <p>True</p> Signup and view all the answers

    Part of alignment includes developing organizational touchpoints for data governance work. Some examples of touchpoints include: Procurement and Contracts; Budget and Funding; Regulatory Compliance; and the SDLC framework.

    <p>True</p> Signup and view all the answers

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

    <p>Manual workflows that indicate where the user send the document</p> Signup and view all the answers

    An image processing system captures, transforms and manages images of paper and electronic documents.

    <p>True</p> Signup and view all the answers

    OCR is the abbreviation for Optical Character Recognition.

    <p>True</p> Signup and view all the answers

    Reference and Master data definition involves managing shared data to meet organizational goals, reduce risks associated with data redundancy, ensure higher quality, and reduce the costs of data integration.

    <p>True</p> Signup and view all the answers

    A goal of reference and master data is to provide an authoritative source of reconciled and quality-assessed master and reference data.

    <p>True</p> Signup and view all the answers

    Reference data management entails the preventative maintenance of undefined domain values, definitions, and the relationship within and across domain values.

    <p>False</p> Signup and view all the answers

    SOR stands for:

    <p>System of Record</p> Signup and view all the answers

    A System of Reference is an authoritative system where data consumers can obtain reliable data to support transactions and analysis, even if the information did not originate in the system reference.

    <p>True</p> Signup and view all the answers

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

    <p>False</p> Signup and view all the answers

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

    <p>True</p> Signup and view all the answers

    Which ISO standard describes the structure and the organization of data quality management?

    <p>ISO 8000</p> Signup and view all the answers

    Please select the correct definition of Data Management from the options below.

    <p>Data Management is the development, execution and supervision of plans, policies, programs and practices that deliver, control, protect and enhance the value of data and information assets throughout their lifecycles.</p> Signup and view all the answers

    Please select the correct definition of Data Management from the options below:

    <p>Data Management is the development, execution and supervision of plans, policies, programs and practices that deliver, control, protect and enhance the value of data and information assets throughout their lifecycles.</p> Signup and view all the answers

    Which ISO standard describes the structure and organization of data quality management?

    <p>ISO 8000</p> Signup and view all the answers

    The accuracy dimension relates to the precision of data values. Is this statement true or false?

    <p>True</p> Signup and view all the answers

    Data Integrity includes ideas associated with completeness, accuracy, and consistency. Is this statement true or false?

    <p>True</p> Signup and view all the answers

    Validity, as a dimension of data quality, refers to whether data values are consistent with a defined domain of values. Is this statement true or false?

    <p>True</p> Signup and view all the answers

    Do data quality issues only emerge at initial stages of the data lifecycle?

    <p>False</p> Signup and view all the answers

    Study Notes

    Data Management Fundamentals

    • The Certified Data Management Professional (CDMP) certification is a recognized credential for data management professionals.
    • To prepare for the exam, it is recommended to study using the DAMA-DMBOK2 guide and take practice quizzes.

    Data Management

    • Data Management is the development, execution, and supervision of plans, policies, programs, and practices that deliver, control, protect, and enhance the value of data and information assets throughout their lifecycles.
    • Data Management Professionals work with both technical and non-technical aspects of data.
    • Data and information are distinct concepts, with data being the raw facts and information being the actionable insights gained from those facts.
    • Principles of data management include recognizing data as an asset, practicing data lifecycle management, and managing data as a liability.

    Data Handling Ethics

    • Data handling ethics involve the responsible and ethical collection, storage, management, use, and disposal of data.
    • Key principles of data handling ethics include respect for persons, beneficence, justice, and transparency.
    • Ethics intersect with data management, including data quality, metadata, and data integration.
    • Risks of unethical data handling include information gaps, biased data, and misleading visualizations.

    Data Governance

    • Data governance is the process of ensuring that data is managed properly, according to policies and best practices.
    • Data governance is focused on how decisions are made about data and how people and processes are expected to behave in relation to data.
    • Data governance includes strategy, policy, data management projects, compliance, and oversight.
    • Goals of data governance include reducing risk, improving processes, and managing data as a liability.
    • Data governance operating models include centralized, decentralized, and federated models.

    Data Architecture

    • Data architecture is the fundamental organization of a system, including its components, relationships, and principles governing its design and evolution.
    • Enterprise Architecture domains include business architecture, data architecture, application architecture, and technology architecture.
    • The goal of data architecture is to bridge between business strategy and technology execution.
    • Data architects facilitate alignment between business and IT, and identify data storage and processing requirements.### Data Architecture
    • Deliverables in the data architecture context diagram include: data flows, enterprise data, implementation roadmap, data value chains, and all of the above.
    • The purpose of enterprise application architecture is to describe the structure and functionality of applications in an enterprise.
    • The dependencies of enterprise technology architecture are that it acts on specified data according to business requirements.
    • Roles associated with enterprise data architecture include: data architect, data modellers, and data stewards.
    • The Zachman Framework's communication interrogative columns provide guidance on defining enterprise architecture:
      • What -> The inventory column
      • When -> The timing column
      • Why -> The motivation column
      • Who -> The responsibility column
      • How -> The process column
    • The highest level model within the enterprise data model is the conceptual model.
    • Data flows map and document relationships between data and: locations where local differences occur, network segments, and applications within a business process.
    • Enterprise data architecture usually includes the following work streams: strategy, organization, results, and working methods.
    • A roadmap for enterprise data architecture describes the architecture's 3 to 5-year development path, guided by a data management maturity assessment.
    • Enterprise data architecture project-related activities include: define scope, design, and implement.

    Data Modelling and Design

    • A deliverable in the data modelling and design context diagram is the logical data model.
    • Inputs in the data modelling and design context diagram include: data standards, data sets, data management architecture, enterprise taxonomy, and data architecture.
    • Data models comprise and contain metadata essential to data consumers.
    • Data models are critical to effective management of data, as they:
      • Provide a common vocabulary around data
      • Capture and document explicit knowledge about an organization's data and systems
      • Serve as a primary communication tool during projects
      • Provide the starting point for customizations, integration, or even replacement of an application
    • Data modelling is most frequently performed in the context of systems and maintenance efforts, known as System Development Lifecycle (SDLC).
    • Category information and business activity information are types of data that can be modelled.
    • Examples of the 'Who' entity category include: employee, patient, player, and suspect.
    • Examples of the 'What' entity category include: product, service, and time.
    • High-quality data definitions exhibit three characteristics: clarity, accuracy, and completeness.
    • The number of entities in a relationship is the arity of the relationship, with the most common being: unary, binary, and ternary.
    • A foreign key is used in physical and sometimes logical relational data modelling schemes to represent a relationship.
    • Valid modelling schemes or notations include: NoSQL, dimensional, relational, object-oriented, and fact-based.
    • Domains can be identified in different ways, including: data type, data format, list, range, and rule-based.
    • Snowflaking is the term given to normalizing the flat, single-table, dimensional structure in a star schema into the respective component hierarchical or network structures.

    Data Storage and Operations

    • Data Storage and Operations involves the design, implementation, and support of stored data to maximize its value.
    • Inputs in the data storage and operations context diagram include: data requirements, service level agreements, data management architecture, data models, and data architecture.
    • The goals of data storage and operations include:
      • Managing performance of data assets
      • Managing the availability of data throughout the data lifecycle
      • Managing the performance of data transactions
    • A database is a collection of stored data, regardless of structure or content.
    • A node is a group of computers hosting either processing or data as part of a distributed database.
    • Service Level Agreement (SLA) is a formal agreement between the service provider and the customer.
    • The database administrator (DBA) is the most established and widely adopted data professional role.
    • DBAs do not exclusively perform all the activities of data storage and operations.
    • An application DBA does not lead the review and administration of procedural database objects.
    • Types of DBA specializations include: application, development, and procedural.
    • Classifications of database types include: centralized and distributed.
    • The CAP theorem asserts that a distributed system cannot comply with all the parts of the ACID, and must instead trade-off between consistency, availability, and partition tolerance.
    • The BASE acronym is made up of: basically available, soft state, and eventual consistency.

    Data Security

    • Data security includes the planning, development, and execution of security policies and procedures to provide authentication, authorization, access, and auditing of data and information assets.
    • The goals of data security practices are to protect information assets in alignment with privacy and confidentiality regulations, contractual agreements, and business requirements.
    • A deliverable in the data security context diagram is the data security architecture.
    • The goals of data security practices come from stakeholders, government regulations, proprietary business concerns, legitimate access needs, and contractual obligations.### Data Security
    • Goals of data security:
      • Enable appropriate access to enterprise data assets
      • Understand and comply with all relevant regulations and policies for privacy and confidentiality
      • Ensure that the privacy and confidentiality needs of all stakeholders are enforced and audited
    • Primary drivers of data security activities:
      • Risk reduction
      • Business growth
    • Data security issues, breaches, and unwarranted restrictions on employee access to data can directly impact operational success
    • Vulnerability is defined as a weakness or defect in a system that allows it to be successfully attacked and compromised
    • Risk classifications describe the sensitivity of the data and the likelihood that it might be sought after for malicious purposes
    • Data integrity is NOT the state of being partitioned – protected from being whole
    • The four A's in security processes include:
      • Audit
      • Authentication
      • Access
      • Authorization
    • Methods for masking data include:
      • Substitution
      • Temporal variance
      • Value variance
    • Concepts that drive security restrictions:
      • Regulation
      • Confidentiality level
    • Device security standards include:
      • Access policies regarding connections using mobile devices
      • Awareness of security vulnerabilities
    • Confidentiality classification schemas might include two or more of the five confidentiality classification levels, including:
      • Internal use only
      • Restricted confidential
      • Confidential
    • Malware types include:
      • Trojan horse
      • Worm
      • Virus
      • Adware
    • Malware refers to any infectious software created to damage, change or improperly access a computer or network
    • Instant Messaging (IM) allows users to message each other in real-time
    • Different levels of policy are required to govern behavior to enterprise security, including:
      • Data security policy
      • IT security policy
      • Enterprise security policy
    • Data access control can be organized at an individual level or group level, depending on the need

    Data Integration and Interoperability

    • Data Integration and Interoperability (DII) describes processes related to the movement and consolidation of data within and between data stores, applications, and organizations
    • DII is dependent on other areas of data management, including:
      • Metadata
      • Data architecture
      • Data governance
      • Data security
      • Data modeling and design
      • Data storage and operations
    • The need to manage data movement efficiently is a primary driver for Data Integration and Interoperability
    • Goals of DII 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
      • Support business intelligence, analytics, master data management, and operational efficiency efforts
    • Deliverables in the DII context diagram include:
      • Data Integration and Interoperability Strategy
      • Data access agreements
    • ETL (Extract, Transform, Load) is a central process in DII
    • Examples of interaction models include:
      • Hub-and-spoke
      • Publish-subscribe
      • Point-to-point
    • Process controls in DII include:
      • Consistency logging
      • Exception logs
      • Alerts
    • Examples of data integration solutions include:
      • Change Data Capture
      • Latency
      • Real-time data integration
    • Types of latency include:
      • Batch
      • Real-time synchronous
      • Distributed

    Document and Content Management

    • E-discovery is the process of finding electronic records that might serve as evidence in a legal action
    • Deliverables in the document and content management context diagram include:
      • Policy and procedure
      • Data governance
      • Content and records management strategy
      • Audit trail and log
    • Goals of implementing best practices around document and content management include:
      • Ensuring effective and efficient retrieval and use of data and information in unstructured formats
      • Ensuring integration capabilities between structured and unstructured data
      • Complying with legal obligations and customer expectations
    • Content refers to the data and information inside a file, document, or website
    • Content management includes the systems for organizing information resources so that they can be stored, retrieved, and used
    • A controlled vocabulary is a defined list of explicitly allowed terms used to index, categorize, tag, sort, and retrieve content
    • Taxonomies can have different structures, including:
      • Polyhierarchy
      • Facet taxonomy
      • Flat taxonomy
    • Information architecture is the process of creating structure for a body of information or content, including:
      • Navigation maps
      • User flows
      • Use cases
    • Most document programs have policies related to:
      • Scope and compliance audits
      • Proper destruction of records
      • Identification and protection of vital records

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    CDMP Data Management Fundamentals Exam Questions PDF

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    Assess your knowledge of Data Management Fundamentals and prepare for the Certified Data Management Professional certification exam with this practice quiz.

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