Data Tech Debt — Learn free

Self-check

Questions 1–10 of 10

1. What does the term 'technical debt' refer to in software development?
Question 1A. The licence fees owed for software toolsB. The implied cost of additional work created when software is built in a way that prioritises short-term goals over long-term sustainabilityC. The number of bugs found during testingD. The interest paid on loans used to buy hardwareE. The time spent training new developers
2. Technical debt is compared to which everyday concept?
Question 2A. A savings account earning interestB. A lottery ticketC. Financial debt, where money borrowed must be paid back with interestD. A rental agreementE. An insurance policy
3. Which type of general technical debt describes rushed or inadequate system architecture that makes adding new features difficult?
Question 3A. Code debtB. Testing debtC. Documentation debtD. Design debtE. Governance debt
4. In the data world, which kind of technical debt is illustrated by rushing to type in students' grades and ending up with inaccurate information, like wrong scores on a report card?
Question 4A. Data quality debtB. Data integration debtC. Data architecture debtD. Data governance debtE. Data documentation debt
5. Information gathered from several friends for a school project does not fit together because their different formats were never reconciled, like a puzzle with mismatched pieces. Which data technical debt does this describe?
Question 5A. Data quality debtB. Data integration debtC. Data governance debtD. Data documentation debtE. Data architecture debt
6. The treehouse analogy, where a poor initial plan makes it hard to add a slide or secret door later, illustrates which data technical debt?
Question 6A. Data quality debtB. Data documentation debtC. Data architecture debtD. Data integration debtE. Data governance debt
7. Which consequence is associated with data governance debt, illustrated by a school with unclear rules?
Question 7A. Faster query performanceB. Data breaches or problems following privacy rules because policies were poorly definedC. Smaller storage requirementsD. Improved documentationE. Fewer data sources to integrate
8. The treasure map analogy, where important clues and rules were never written down, illustrates which data technical debt?
Question 8A. Data architecture debtB. Data quality debtC. Data integration debtD. Data governance debtE. Data documentation debt
9. Which of the following is NOT listed as a way of addressing technical debt in the data world?
Question 9A. Implementing better data quality checksB. Redesigning data architecturesC. Enhancing data governance practicesD. Ignoring the shortcuts until the system failsE. Creating comprehensive documentation
10. Which factors are given as causes of technical debt arising?
Question 10A. Tight deadlines, evolving requirements or a need for rapid prototypingB. Too much documentation and excessive testingC. Overly slow delivery and long planning phasesD. Using only open-source softwareE. Having too many data sources