GenAI in Finance Software Testing CT-FT Practice Questions (Page 1)

Questions 1–5

1. 1expert · hard

   A testing team is considering using GenAI to generate test data for a new credit card fraud detection system. The system uses machine learning to identify fraudulent transactions. The team is concerned about the quality of the generated data. What is the most important limitation of GenAI that they should consider?

   • AGenAI cannot be used for fraud detection testing.
   • BGenAI may generate data that is not representative of real fraud patterns.
   • CGenAI cannot generate data for machine learning systems.
   • DGenAI will always generate biased data.
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2. 2foundation · medium

   In functional testing of a finance application, how can GenAI be used to generate test cases?

   • ABy automatically fixing all functional defects found during testing.
   • BBy replacing the need for any test data management.
   • CBy analyzing requirements and user stories to propose relevant test scenarios.
   • DBy directly executing test cases on the production environment without any setup.
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4. 3foundation · medium

   What is a core limitation of Generative AI when applied to finance software testing?

   • AIt eliminates the need for human testers in finance software testing.
   • BIt cannot generate realistic test data for financial applications.
   • CIt always requires a dedicated GPU cluster to run any testing task.
   • DIt may produce outputs that are factually incorrect or not traceable to source requirements.
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5. 4application · medium

   A QA team is using GenAI to generate test scripts for a mobile banking app. They want to ensure the scripts are correct and comply with internal standards. What is the most effective way to integrate human oversight?

   • ARun the generated scripts automatically and only review failures.
   • BRely on the GenAI tool's built-in validation features to ensure compliance.
   • CUse a second GenAI tool to review the scripts generated by the first tool.
   • DHave a senior tester review all generated test scripts before they are executed.
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6. 5expert · hard

   A large bank is implementing a GenAI-based test case generation tool for its core banking system. The tool is trained on a mix of public code and internal test cases. The bank is subject to strict regulatory requirements regarding data privacy and auditability. The QA lead is concerned about the tool's output being used as evidence in regulatory audits. What is the most critical action to take?

   • AImplement a process to document the generation, review, and approval of all AI-generated test cases.
   • BDisable the use of GenAI for test case generation to avoid regulatory risks.
   • CEnsure that all generated test cases are reviewed and approved by a human tester before use.
   • DUse only the internal test cases for training to avoid any external data risks.
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