Auditing · Topic 14

Emerging Areas in Auditing Quiz

Work through 25 multiple-choice questions, one at a time. Submit each answer for feedback. At the end, review your score and see which concepts to revisit.

Review the Q&AAuditing quiz learning path
1. A sustainability assurance engagement covers greenhouse gas emissions across specified sites. Which auditing concept best fits this case?
2. A machine-learning tool flags transactions as suspicious but cannot explain several flags. Which auditing concept best fits this case?
3. A business claims cybersecurity controls are effective solely because it bought antivirus software. Which auditing concept best fits this case?
4. Digital asset transactions appear on a public blockchain, but management may not control the addresses. Which auditing concept best fits this case?
5. A continuous audit dashboard produces daily exceptions using one automated rule. Which auditing concept best fits this case?
6. A sustainability assurance engagement covers greenhouse gas emissions across specified sites. What is the most appropriate response?
7. A machine-learning tool flags transactions as suspicious but cannot explain several flags. What is the most appropriate response?
8. A business claims cybersecurity controls are effective solely because it bought antivirus software. What is the most appropriate response?
9. Digital asset transactions appear on a public blockchain, but management may not control the addresses. What is the most appropriate response?
10. A continuous audit dashboard produces daily exceptions using one automated rule. What is the most appropriate response?
11. A sustainability assurance engagement covers greenhouse gas emissions across specified sites. Which evidence would be most directly useful?
12. A machine-learning tool flags transactions as suspicious but cannot explain several flags. Which evidence would be most directly useful?
13. A business claims cybersecurity controls are effective solely because it bought antivirus software. Which evidence would be most directly useful?
14. Digital asset transactions appear on a public blockchain, but management may not control the addresses. Which evidence would be most directly useful?
15. A continuous audit dashboard produces daily exceptions using one automated rule. Which evidence would be most directly useful?
16. A sustainability assurance engagement covers greenhouse gas emissions across specified sites. Which conclusion is best supported by the case?
17. A machine-learning tool flags transactions as suspicious but cannot explain several flags. Which conclusion is best supported by the case?
18. A business claims cybersecurity controls are effective solely because it bought antivirus software. Which conclusion is best supported by the case?
19. Digital asset transactions appear on a public blockchain, but management may not control the addresses. Which conclusion is best supported by the case?
20. A continuous audit dashboard produces daily exceptions using one automated rule. Which conclusion is best supported by the case?
21. A sustainability assurance engagement covers greenhouse gas emissions across specified sites. Which shortcut would most directly undermine the work in this case?
22. A machine-learning tool flags transactions as suspicious but cannot explain several flags. Which shortcut would most directly undermine the work in this case?
23. A business claims cybersecurity controls are effective solely because it bought antivirus software. Which shortcut would most directly undermine the work in this case?
24. Digital asset transactions appear on a public blockchain, but management may not control the addresses. Which shortcut would most directly undermine the work in this case?
25. A continuous audit dashboard produces daily exceptions using one automated rule. Which shortcut would most directly undermine the work in this case?
Show the answer key
  1. Sustainability assurance Assurance conclusions relate to the specified subject matter and criteria.
  2. AI-assisted audit procedures A model output requires evaluation before it can support a conclusion.
  3. Cybersecurity control assessment Buying a tool does not by itself demonstrate effective operation.
  4. Digital asset rights and obligations An on-chain balance alone does not establish the reporting entity’s rights.
  5. Continuous auditing Frequent monitoring can improve timeliness but is limited by rule coverage.
  6. Define the reporting boundary, criteria and level of assurance before testing Assurance conclusions relate to the specified subject matter and criteria.
  7. Validate inputs and outputs and use human review of flagged items A model output requires evaluation before it can support a conclusion.
  8. Define the relevant threats and test selected controls against appropriate criteria Buying a tool does not by itself demonstrate effective operation.
  9. Corroborate control or custody with procedures suited to the arrangement An on-chain balance alone does not establish the reporting entity’s rights.
  10. Validate rule coverage and investigate and track exceptions through resolution Frequent monitoring can improve timeliness but is limited by rule coverage.
  11. Emissions source data, methods and boundary documentation Define the reporting boundary, criteria and level of assurance before testing.
  12. Documented validation cases, known limitations and corroborating evidence Validate inputs and outputs and use human review of flagged items.
  13. Configuration records, incident logs and tests of coverage and response Define the relevant threats and test selected controls against appropriate criteria.
  14. Custody agreements, suitable control evidence and blockchain records Corroborate control or custody with procedures suited to the arrangement.
  15. Rule definitions, population checks and exception follow-up records Validate rule coverage and investigate and track exceptions through resolution.
  16. Assurance conclusions relate to the specified subject matter and criteria Emissions source data, methods and boundary documentation would support this assessment.
  17. A model output requires evaluation before it can support a conclusion Documented validation cases, known limitations and corroborating evidence would support this assessment.
  18. Buying a tool does not by itself demonstrate effective operation Configuration records, incident logs and tests of coverage and response would support this assessment.
  19. An on-chain balance alone does not establish the reporting entity’s rights Custody agreements, suitable control evidence and blockchain records would support this assessment.
  20. Frequent monitoring can improve timeliness but is limited by rule coverage Rule definitions, population checks and exception follow-up records would support this assessment.
  21. Treating the engagement as verification of every environmental claim Assurance conclusions relate to the specified subject matter and criteria.
  22. Relying on the model’s label as proof that an entry is fraudulent A model output requires evaluation before it can support a conclusion.
  23. Accepting an invoice for the software as sufficient evidence of effectiveness Buying a tool does not by itself demonstrate effective operation.
  24. Equating an observable wallet balance with legal ownership An on-chain balance alone does not establish the reporting entity’s rights.
  25. Assuming daily processing detects every type of misstatement Frequent monitoring can improve timeliness but is limited by rule coverage.

These cases use general auditing principles. Apply the relevant local law and professional standards when evaluating a real engagement. Reference: IAASB standards and pronouncements.