Enterprise Data Modelling — Learn free

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Questions 1–10 of 10

1. What is Enterprise Data Modelling?
Question 1A. The practice of designing one database per projectB. The practice of tuning enterprise serversC. The practice of creating a single, high-level view of data for the entire organisationD. The practice of documenting ETL jobs across systemsE. The practice of choosing an enterprise reporting tool
2. What is the main goal of Enterprise Data Modelling?
Question 2A. To reduce the number of serversB. To speed up a single application's queriesC. To provide a consistent framework for managing data across the whole enterpriseD. To replace all departmental systems with one systemE. To define workflow diagrams
3. How is Enterprise Data Modelling characterised in terms of its role?
Question 3A. As a detailed design for a single applicationB. As a physical storage planC. As a temporary project deliverableD. As a strategic blueprint rather than a detailed design for one applicationE. As a low-level technical specification
4. In the school example of Enterprise Data Modelling, what problem arises when admissions, exams and fees systems each define a student differently?
Question 4A. The systems cannot be installedB. Students cannot enrolC. The databases become too largeD. Reports will not matchE. Exams cannot be marked
5. In the banking example, which attributes are listed for the Loan entity?
Question 5A. Customer identifier, name, date of birth and contact informationB. Payment date and payment amountC. Branch code and manager nameD. Credit score and risk categoryE. Loan amount, term, interest rate and status
6. Which of the following is NOT one of the banking systems named as sharing the enterprise definitions of Customer, Loan and Repayment?
Question 6A. Loan originationB. Risk managementC. Core bankingD. CollectionsE. Human resources
7. Why does consistency matter even more when data is analysed at an enterprise level?
Question 7A. Because analytics runs on faster hardwareB. Because measures such as loan amount, approval rate or portfolio risk must mean the same thing in finance, risk and sales reportsC. Because analysts prefer fewer tablesD. Because enterprise data is always stored in a data lakeE. Because reports are printed on paper
8. Which statement about the nature of Enterprise Data Modelling is correct?
Question 8A. It is project-specific and tied to one platformB. It is strategic, technology-independent and can scale across operational systems, data warehouses and data lakesC. It focuses on short-term deliveryD. It only applies to data stored in data warehousesE. It is a physical modelling technique
9. What phrase describes the value Enterprise Data Modelling adds by eliminating conflicting definitions such as differing meanings of active loan?
Question 9A. A single version of the truthB. A bounded contextC. A data swampD. A ubiquitous languageE. A star schema
10. Which initiatives are said to depend on the shared foundation that Enterprise Data Modelling provides?
Question 10A. Data governance, master data management and large-scale digital transformationB. Server virtualisation and network upgradesC. Email migration and desktop supportD. Payroll processing and recruitmentE. Website design and social media marketing