Every enterprise is subject to many governmental and industry regulations, many of which regulate how data and information is used and managed. Part of the Data Governance Function is to: This is a risk and audit responsibility; Data Governance plays no role in this Perform ad-hoc audits of possible regulations to report to the DG Council on an information only basis This is about data; Data Governance is accountable for the whole process, with Risk and Audit reporting to DG Monitor and ensure that organisations meet any regulatory compliance requirements Enforce enterprise-wide mandatory compliance to regulations.
In the Information Management Lifecycle, the Data Governance Activity 'Define the Data Governance Framework' is considered in which Lifecycle stage? Enable Create and Acquire Specify Plan Maintain and Use.
Which of these is NOT a standard motivation for Data Governance? Decentralised Governance Devolved governance Pre-emptive governance Proactive governance Reactive governance.
Which of these are NOT true of Data Governance? DG is the exercise of authority and control over the management of data assets DG is a continuous process of data improvement IT is a key stakeholder in DG A DG initiative should always be led by the IT department There are different organization models for DG.
When new governmental and industry regulations are formulated and enacted, Data Governance plays a key role in the process of identifying the data and information components for compliance. What do you see as their most important role in any regulatory compliance project? Working with business and technical leadership to find the best answers to a standard set of regulatory compliance questions (How, Why, When, etc) Provide access to any possible data set to the compliance team and allow them to mine the data for non-compliance Create a DG 'in-house' project with a team of data stewards to create a standard response Take no part in any project at all, declaring it an audit and risk project Work in isolation and mine the data and information for compliance and non-compliance issues.
An umbrella term for any classification or controlled vocabulary is: Data model Taxonomy English Dictionary Metadata
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The needs of data protection require us to ensure that: Data can always be freely used in the company as it is a company asset Data is processed only in ways compatible with the intended and communicated use it was collected for, and respects the consent of the data subject Data is encrypted at all times Data is secured with a password Data is frequently backed up so that it can be recovered in all cases.
Documents and records should be classified based on the _______ level of confidentiality for information found in the record. Overall General Average Highest.
A goal of 'Document and Content Management' is to ensure effective and efficient retrieval and use of: Data and information in unstructured format Information, but not data in unstructured formats Data, but not information in unstructured formats Data and information in relational formats Data and information in structured formats.
Which of the following are primary deliverables of proper document and record management? Managed records in many media formats, e-discovery records, policies and procedures, contracts and financial documents Spreadsheets, company library books, sales transactions Local drives of laptops, transcripts of phone calls Relational databases, database logs, paper documents Data from tracking devices, building sensor data.
Which of these is NOT a type of key found in a data model? Primary key Local key Foreign key Alternate key Surrogate key.
The highest level of these data model types is the:
Conceptual model Dimensional model Logical model Physical model Database model.
Dimension tables: Contain measures Do not contain hierarchies Have many columns but few rows Have few columns but many rows Are the same as Facts.
In a recursive relationship: None of these, recursive relationships are not allowed in Data Models The foreign key must have a role name to avoid attribute duplication The relationship could be mandatory at either end All of the above The relationship could be an identifying relationship.
Identify who has primary responsibility for data capture and usage design within programs. Suppliers, Consumers DM Executive, BI Analysts, Data Security Administrator Data Architects, Data Analysts,Database Administrators Business Data Stewards, Subject Matter Experts (SMEs) Software Architects, Developers.
A data quality program should limit its scope to: The highest profile program with the best benefits The data most critical to the enterprise and its customers The data that changes most often The data that is of interest to the Chief Executive Officer All the data stored in the enterprise.
The Data Quality Management cycle has four stages. Three are Plan, Monitor and Act. What is the fourth stage? Improve Reiterate Deploy Manage Prepare.
A Data Quality Service Level Agreement (SLA) would normally include which of these? A breakdown of the costs of data quality improvement An enterprise data model Detailed technical specifications for data transfer Respective roles & responsibilities for data quality A Business Case for data improvement.
Data quality measurements can be taken at three levels of granularity. They are: Departmental data, regional data, and enterprise data Data element value, data instance or record, and data set Historical data, current data and future dated data Person data, location data, and product data Fine data, coarse data, and rough data.
A Data Quality dimension is: A valid value in a list A measurable feature or characteristic of data One aspect of data quality used extensively in data governance The value of a particular piece of data A core concept in dimensional modelling.
How does Data Security contribute to competitive advantage? Governments do not allow organisations to trade if they do not manage Data Security Data security stops organisations going out of business due to an information leak Data Security makes it harder for your competitors to find out about who you do business with Data Security helps to protect proprietary information and intellectual property, as well as customer and partner information Data Security makes your competitors invest more effort into trying to find out your trade secrets.
Which of these are characteristics of an effective data security policy? The defined procedures are tightly defined, with rigid and effective enforcement sanctions, and alignment with technology capabilities The defined procedures ensure that the right people can use and update data in the right way, and that all inappropriate access and update is restricted The policies are specific, measurable, achievable, realistic, and technology aligned The procedures defined are benchmarked, supported by technology, framework based, and peer reviewed None of these.
Which of the following define the data security touch points in an organisation? Internal Audit Business rules and process workflow Industry standards Risk Assessment Legislation.
What is the role of the Data Governance Council in defining an Information Security policy? The Data Governance Council should review and approve the high-level Data Security Policy The Data Governance Council should draft early versions of the Data Security Policy The Data Governance Council should have no role in Data Security The Data Governance Council should implement the Data Security Policy The Data Governance Council should define the Data Security Policy.
A CRUD matrix helps organisations map responsibilities for data changes in the business process work flow. CRUD stands for Create, Review, Use, Destroy Cost, Revenue, Uplift, Depreciate Create, Read, Update, Delete Create, React, Utilise, Delegate Confidential, Restricted, Unclassified, Destroy.
A data lineage tool enables a user to: Visualize how the data gets to the data lake Track the historical changes to a data value Enables rapid development of dashboard reporting Line up the data to support sophisticated glossary management Track the data from source system to a target database; understanding its transformations.
When performing an evaluation of analytic applications, which of the following questions is least relevant to identify the level of effort needed? The Standard source systems for which ETL is supplied Annual costs such as license, maintenance, etc. How much of the tool infrastructure meets our organisational infrastructure How much do the canned processes in the tool match our business Number of source systems we need to integrate into the tool.
Critical to the success of the data warehouse is the ability to explain the data. The DMBoK knowledge area that practices these techniques is: Document & Content Management Metadata Management Reference and Master Data Data Storage and Operations Data Architecture.
One of the difficulties when integrating multiple source systems is: Modifying the source systems to align to the enterprise data model Completing the data architecture on time for the first release Maintaining documentation describing the data warehouse operation Determining valid links or equivalences between data elements Having a data quality rule applicable to all source systems.
Which of the following is not a good example of BI? Strategic Analytics for Business Decisions Statutory reporting to a Regulatory Body Supporting Risk Management Decision Reporting Decision Support Systems.
A strong argument for pursuing a Reference Data and/or Master Data management initiative is: Job security for the data people They are essential functions in the data management framework It will not require a lot of effort
By centralizing the management of Reference and Master data, the organization can conform critical data needed for analysis It will not require a lot of time.
Which of these is a valid definition of Reference Data? Data that has a common and widely understood data definition Data that is widely accessed and referenced across an organisation Data that provides metadata about other data entities Data used to classify or categorize other data Data that is fixed and never changes.
Which of the following statements regarding a value domain is FALSE? A value domain provides a set of permissible values by which a data element can be implemented More than one set of reference data value domains may refer to the same conceptual domain Conforming value domains across the organization facilitates data quality Value domains are defined by external standard organizations A value domain is a set of allowed values for a given code set.
Which of the following is NOT a primary Master Data Management area of focus? Identifying duplicate records Producing read only versions of key data items Providing access to golden data records Generating a golden record/best version of the truth Producing clear data definitions for Master Data.
Which one of the following statements is true? Reference Data Management involves identifying the 'best' or 'golden' record for each domain Master Data Management requires techniques for splitting or merging an instance of a business entity Master Data Management involves identifying and maintaining approved coded values Business data stewards maintain lists of valid data values for master data instances Managing reference data requires the same activities and techniques as does managing master data.
What type of Meta-Data provides developers and administrators with knowledge and information about systems? Process Meta-Data Business Meta-Data Unstructured Meta-Data Data Stewardship Meta-Data Technical Operational Meta-Data.
What would you not expect to find in the MetaData repository? Data Models Data Requirements Data Lineage diagrams and models Data storage devices Data Dictionary.
The role of the Conceptual data model in the Metadata repository is: To agree the cardinality and optionality of relationships between all entities To summarize the key data subject areas for a business area at a high level of abstraction to enable the major data concepts to be understood To determine the primary, alternate and foreign keys of entities None of these All of these.
We would expect to consult the Metadata Library when: Formulating a Goverance policy Accessing the internet Selecting a Data Storage device Assessing the impact of change Implementing a Data Quality tool.
Data Governance touch points throughout the project lifecycle are facilitated by this organization. The Project Management Office The Data Governance Office The Master Data Office The Data Stewards Office The Data Governance Steering Committee.
What Organization Structure should set the overall direction for Data Governance? Data Quality Board Data Governance Council PMO Data Governance Office IT Leadership Team.
Who is most responsible for communicating and promoting awareness on the value of Data Governance in the organization? Everyone in the Data Management Community Data Champions The Data Governance Council Data Owners and Stewards The Data Governance Office.
When defining your business continuity plan, which of the following should one consider doing? Have the contracts in place to acquire new hardware in case of technical problems, define policies Consider written policies and procedures, impact mitigating measures, required recovery time and acceptable amount of disruption, the criticality of the documents Make sure that the data is retained sufficiently long, check that critical data is encrypted, check access rights Determine the risk, probability and impact, check document backup frequency Write a report and discuss with management the required budget.
All of the following are TRUE statements on relationship types except: A one-to-many relationship says that a child entity may have one or more parent entities A recursive relationship relates instances of an entity to other instances of the same entity A many-to-many relationship says that an instance of each entity may be associated with many instances of the other entity, and vice versa A one-to-one relationship says that a parent entity may have one and only one child entity A one-to-many relationship says that a parent entity may have one or more child entities.
What are relationship labels? The verb phrases describing the business rules in each direction between two entities A non-identifying relationship A foreign key that has been role-named A relationship without cardinality The nullability setting on a foreign key.
What is the difference between cardinality rules and data integrity rules? Referential integrity rules only appear on a relational data model, and cardinality rules only appear on a dimensional data model Referential integrity rules quantify the relationships between two or more entities, and cardinality rules quantify the common attributes across entities Cardinality rules define the quantity of each entity instance that can participate in a relationship between two entities, and referential integrity rules ensure valid values There is no difference. Cardinality rules and Referential integrity rules are synonyms Referential integrity rules define the quantity of each entity instance that can participate in a relationship between two entities, and cardinality rules ensure valid values.
In Dimensional data models, which of these is NOT true regarding Measures? Just because a value is numerical does not mean it is a measure Care must be taken if a measure is a snapshot figure Measures can always be added across all dimensions Measures are found in Fact tables Measures should be numeric and additive.
Which of the following is NOT a stage in the Shewhart / Deming Cycle that drives the data quality improvement lifecycle? Do Investigate Act Check Plan.
Which of these statements is true? Data Quality Management only addresses structured data Data Quality Management is the application of technology to data problems Data Quality Management is a synonym for Data Governance Data Quality Management is usually a one-off project Data Quality Management is a continuous process.
Which of the following is NOT usually a feature of data quality improvement tools? Transformation
Parsing Standardization Data modelling Data profiling.
A RACI matrix is a useful tool to support the ______ in an outsourced arrangement. Alignment of Business goals Service level Agreement Attributing Costs Segregation of duties Transfer of access controls.
Which of these statements best defines Data Security Management? None of these The definition of controls, technical standards, frameworks, and audit trail capabilities to identify who has or has had access to information The planning, implementation, and testing of security technologies, authentication mechanisms, and other controls to prevent access to information The planning, development, and execution of security policies and procedures to provide proper authentication, authorization, access, and auditing of data and information assets The implementation and execution of checkpoints, checklists, controls, and technical mechanisms to govern the access to information in an enterprise.
Which of these are increasingly driving legislation for information security and data privacy? A desire for economic protectionism An objective of making life more challenging for information management professionals A recognition of Ethical issues in information management A resistance to open data and transparency GDPR.
Which approach is considered most effective when supporting multi-dimensional business report requests? OLTP EDI BI OLAP ODS.
Slice, Dice, Roll-up and Pivot are terms used in what kind of data processing? OLAP EDI ODS OLTP EIEIO.
You need to discover possible relationships or to show data patterns in an exploratory fashion when you do not necessarily have a specific question to ask. What kind of data tool would you use to identify patterns of data using various algorithms? Data Visualisation Application ETL Jobs Data Quality Profiler Meta-Data Data Lineage View Data Mining.
A Data Integration approach that updates a Data Warehouse with small changes from Operational systems is called: EII SOA ELT CDC ETL.
Master Data Management: Controls the definition of business entities Ensures coded values are always used Is time-consuming with questionable impact on data quality Allows applications to define business entities as needed and manages the mappings between common data in a central location
Is synonymous with Reference Data Management.
Plant Equipment is an example of: Master Data None of these Reference Data Inverted Data Transaction Data.
A common driver for initiating a Reference Data Management program is: It will consolidate the process of securing third party code sets It will improve data quality and facilitate analysis across the organization It can be a one-time-only project Managing codes and descriptions requires little effort and low cost It fosters the creative use of data.
Reference Data: Usually has fewer attributes than Master Data Is more difficult to Govern than Master Data Usually has more attributes than Master Data Is also known as External data Is free.
To which of the following initiatives was the establishment of an industry Meta-Data Standard essential? Proprietary XML EDI BASEL II/ SOX Internet Protocols JSON.
Metadata repository processes will not include: All of these Managing change to data products (e.g. Data Dictionary or Business Data Glossary) entries e.g. new data term to be defined, new data requirement, new database tables added, new system included into the technical landscape Controlling versions of data product will be required to manage the required single published master copy in conjunction with the variants potentially established as work in progress Selecting Data Management Library software, search, and storage technologies Assessing impact where change to existing data product entries are proposed e.g. the impact of change on related data on other systems.
What are the primary responsibilities of a data steward? The manager responsible for writing policies and standards that define the data management program for an organization Identifying data problems and issues The data analyst who is the subject matter expert (SME) on a set of reference data Analyzing data quality A business role appointed to take responsibility for the quality and use of their organization's data assets.
Which of these does NOT characterize an effective data steward? He / she works collaboratively across the organization with data stakeholders and others identifying data problems and issues He / She works in association with the Data Owner to protect and enhance the data assets under his or her control Is a highly experienced technical expert in a variety of data management disciplines & tools He / She is an effective communicator Is a recognized subject matter expert in the data subject area / business domain that he or she is responsible for.
A document management system is an application used to track and store electronic documents and electronic images of paper documents which provides the following capabilities: Local disk storage and indexing of documents Securing forwarding of documents to colleagues, never having to dispose of documents Scanning and transcoding of documents Storage, versioning, security, meta-data management, indexing and retrieval Wiki, collaboration, online editing.
Non value-added information is often not removed because: We might need the information at a later stage Data is an asset. It is likely to be recognized as valuable in the future Legislation is unclear on what should be kept The policies are unclear of what is defined as non-value-added, storage is cheap so there is no cost driver, and it takes more effort to dispose than to keep It should not be removed. All data is value-added.
In 2009, ARMA International published GARP for managing records and information. GARP stands for: Gregarious Archive of Recordkeeping Processes Generally Available Recordkeeping Practices Global Accredited Recordkeeping Principles Generally Acceptable Recordkeeping Principles G20 Approved Recordkeeping Principles.
Components of logical data models include: Entities All of the above Keys Relationships Attributes.
In the conceptual data model an instantiation of a particular business entity is described as: Dataset Entity occurrence Rule Record Row.
Which of these is NOT a typical activity in Data Quality Management? Enterprise Data Modelling Analysing data quality Identifying data problems and issues Defining business requirements and business rules Creating inspection and monitoring processes.
Which of the following is the best example of the data quality dimension of 'consistency'? The phone numbers in the customer file do not adhere to the standard format The customer file has 50% duplicated entries The source data for the end of month report arrived 1 week late The revenue data in the dataset is always $100 out All the records in the CRM have been accounted for in the data warehouse.
Which of these is a key process in defining data quality business rules? Separating data that does not meet business needs from data that does De-duplicating data records Matching data from different data sources Producing data management policies Producing data quality reports & dashboards.
When outsourcing information management functions, organisations can: Transfer control but not accountability Reduce cost of compliance and improve turnaround Transfer accountability but not control Align strategy and control privacy Improve controls while reducing costs.
Apart from security requirements internal to the organisation, what other strategic goals should a Data Security Management system address? None of these Compliance with ISO29100 and PCI-DSS Regulatory requirements for privacy and confidentiality AND Privacy and Confidentiality needs of all stakeholders Ensuring the organisation doesn't engage in SPAM marketing Compliance with ISO27001 and HIPPA.
In its broadest context, the data warehouse includes: Data stores and extracts that can be transformed into star schemas Either an Inmon or Kimball approach All the data in the enterprise An integrated data store, ETL logic, and extensive data cleansing routines Any data stores or extracts used to support the delivery for BI purposes.
Critical to the incremental development of the data warehouse is: The assurance to include velocity, variety and veracity measurement A strong incident management process A strong capacity management process A strong release management process An agile development team.
One of the key differences between operational systems and data warehouses is: Operational systems focus on business processes; data warehouses focus on business strategies Operational systems focus on data quality; data warehouses focus on data security Operational systems are available 24x7; data warehouses are available during business hours Operational systems focus on current data; data warehouses contain historical data Operational systems focus on historical data; data warehouses contain current data.
Reference Data: (2) Is used to categorize and classify other data Has limited value Is always supplied by outside vendors Has obvious definitions When incorrect has a greater impact than errors in Master and Transaction data.
What is a common motivation for Reference and Master Data Management? Regulatory acts such as BCBS239, GDPR and SOX The need to build a Data Dictionary of all core data entities & attributes The need to consolidate all data into one physical database Business Intelligence and Data Warehousing The need to improve data quality and data integrity across multiple data sources.
A type of Master data architecture is: Repository Virtualised Hybrid All of the above Registry.
We do not expect to consult the MetaData repository when: Assessing the impact of change Updating the operating system that the Master Data management toolset is running on None of the these Investigating a data issue Undertaking a data quality assessment.
A business perspective product in the MetaData repository is: Data Glossary Systems Inventory Physical Data Model Data Dictionary ETL flow.
The number of artifacts that must be searched in the Metadata repository for all Business change projects are: There is no mandatory number of artefacts to be searched but it is highly recommended that the library is examined Conceptual data models and the Business Data Glossary must be examined The Business Data Glossary and Systems Inventory must be consulted Conceptual, Logical and Physical models must be examined The Business Data Glossary and Data Dictionary must be examined.
Which statement best describes the relationship between documents and records? Records are a sub-set of documents Documents are written and records are audio Documents and records are the same thing Documents and records are not related Documents are a sub-set of records
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Which of these describes activities in the document/record management lifecycle? Acquisition, editing, storage, printing, backup, disposal Acquisition, classification, storage, purging Storage, disposal, managing access Encryption, backup, disposal, extraction Identification, management of policies, classification, retention, storage, retrieval and circulation, preservation and disposal.
In a non-identifying relationship: The foreign key of the parent entity migrates to the child entity The primary key of the parent entity becomes a foreign key in the child entity The primary key of the child entity is removed The primary key of the parent entity becomes part of the primary key of the child entity The primary key of the child entity is concatenated.
Complete the following statement. A business rule: Defines constraints on what can and cannot be done Measures a business process Identifies an entity instance Only exists at the level of the physical data model Defines an entity.
Which of these statements has the most meaningful relationship label? An order line contains orders An order is connected with order lines An order is related to order lines An order is associated with order lines An order is composed of order lines.
What is Manual Directed Data Quality Correction? The use of automated cleanse and correction tools with results manually checked before committing outputs Using a data quality improvement manual to guide data cleanse and correction activities Teams of data correctors supervised by data subject matter experts The automation of all data cleanse and correction routines The use of spreadsheets to manually inspect and correct data.
Master data differs from Reference data in the following way: Master data should be held to a higher data quality standard than Reference data Unlike Reference data, Master data is not usually limited to predefined domain values Master data is stipulated and controlled by Data Governance where Reference data is not Master data does not require a data steward Master data does not require business definitions.
Which of these statements are true about Metadata? Data models are components of a Metadata repository The repository is always a decentralized architecture A Metadata repository and a Glossary are synonyms The repository is always a centralized architecture The repository is always a hybrid architecture.
The library of information about our data (our metadata) is built so that: We can better understand it We can be consistent in our use of terminology We can have a shared formalized view of requirements (e.g. what data quality we need) All of these We can better manage it.
Which of these is NOT an expected role of a Data Quality Oversight Board? Setting data quality improvement priorities Data profiling & analysis Producing certification & compliance policies Developing & maintaining data quality Establishing communications & feedback mechanisms.
Stakeholders whose concerns must be addressed in data security management include: All of these External Standards organisations, Regulators, or the Media Clients, Patients, Citizens, Suppliers, or Business Partners Media analysts, Internal Risk Management, Suppliers, or Regulators.
A comparatively new architectural approach is where volatile data is provisioned in a data warehouse structure to provide transactional systems with a combination of historical and near real time data to meet customer needs. This is a definition of: On Line Transactional Processing System Behavioural Decision Support Systems Operational Data Store Active Data Warehousing On Line Analytical Processing Cube.
Which of these is a valid definition of Master Data? Data about the business entities that provide context for business transactions Data that rarely, if ever, changes Data that if missing or incorrect will cause transactions and processes to fail Data that is only held in one data source Data that other data sits hierarchically beneath.
The role of the Physical data model in the Metadata repository is: When the duplicated records were merged To describe how and where our data is stored in our systems applications or packages What the business definition of data concepts is How many master data records are stored in our MDM system Which version of COTS software (E.g. SAP) is implemented.
These are examples of which type of Meta-Data: Data Stores and Data Involved, Government/ Regulatory Bodies; Roles and Responsibilities; Process Dependencies and Decomposition? Business Meta-Data Technical Meta-Data Operational Meta-Data Data Stewardship Meta-Data Process Meta-Data.
According to the DAMA DMBoK, the Data Governance Council (DGC) is the highest authority organization for data governance in an organization. Who should typically chair this Council? Chief Data Steward (Business) / Chief Data Officer The Chief Information Officer (CIO) The Chief Data Architect Any Executive / C-level participant in the DGC The chair should rotate across the Data Owners.
Which of the following is not a step in the 'document and content management lifecycle'? Manage versions and control Create a content strategy Capture records and content Manage retention and disposal Audit documents and records.
What is the difference between an Industry and a Consensus Meta-Data Standard? The terms are used interchangeably to describe the same concept Consensus standards are formed by an international panel of experts whereas industry standards are dictated by a panel of vendors Industry Standards refer to internationally approved global standards such as ISO whereas consensus standards refer to those agreed to within an organisation Industry standards are determined by regulators within a given global region and consensus standards are agreed on the Data Governance Council within an organisation Consensus standards are formed by government legislation whereas industry standards evolve from best practice.
How do data management professionals maintain commitment of key stakeholders to the data management initiative? Rely on the stakeholder group to be self-sustaining Continuous communication education and promotion of the importance and value of of data and information assets Find and deliver benefits to the stakeholders early in the initiative It is not necessary, as the stakeholders signed up at the beginning of the program Weekly email reports showing metrics on data management progress/ lack thereof.
Top down' and 'bottom up' data analysis and profiling is best done in concert because: It gives something for the architects to do while the profilers get on with the work It allows the profiler to show the business the true state of the data Data quality tools are more productive when they are effectively configured It gets everyone involved It balances business relevance and the actual state of the data.
According to the DMBoK, the system that contains the best version of the Master Data is the: System of record Consuming system Source system Spoke Golden record.
The MetaData repository enables us to establish multiple perspectives of data. These are: Structured and unstructured Business and Technical Perspective 3rd normal form and un normalised Dimensional and non dimensional perspective Internal and External.
Definition of Data Security Policies should be: Reviewed by external Regulators Determined by external Regulators A collaborative effort between Business and IT Conducted by external consultants Based on defined standards and templates.
Definition of Data Security Policies should be: Descriptive Meta-Data Structural Meta-Data Administrative Meta-Data Preservation Meta-Data Business Meta-Data.
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