Test Design and Test Implementation with Generative AI CT-GENAI Practice Questions (Page 2)

Questions 6–10

1. 6application · medium

   A team is using a generative AI tool to create test cases from a user story that describes a login feature with password reset. The AI generates a set of test cases that cover the happy path and several invalid-password scenarios, but it does not include any test for the 'Forgot password' link. What should the team do to improve the test case set?

   • AManually add a test case for the 'Forgot password' link and then review the entire set for completeness.
   • BRegenerate the test cases with a different prompt, then use the new set without further review.
   • CDelete the AI-generated test cases and write all test cases manually to ensure full coverage.
   • DAccept the AI-generated set as-is because it covers the main functionality.
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2. 7application · medium

   A team uses a generative AI tool to create test cases from a requirements document. The AI generates 50 test cases, but the team notices that many are duplicates or test the same condition with slightly different wording. What is the best course of action?

   • ARun all 50 test cases to ensure maximum coverage
   • BUse the AI tool to automatically remove duplicates without human review
   • CKeep the duplicates because they provide redundancy in case one fails
   • DReview the test cases, remove duplicates, and consolidate overlapping ones
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4. 8application · medium

   A tester needs to generate test data for a system that has a field for 'country' and a field for 'phone number'. The system validates that the phone number format matches the selected country. The tester uses a generative AI tool to create test data. What is the best way to ensure the generated data is realistic and valid?

   • AUse a fixed country and generate random phone numbers
   • BProvide the AI with a list of countries and their corresponding phone number formats
   • CGenerate phone numbers first and then assign a country that matches the format
   • DGenerate country and phone number independently and let the system handle validation
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5. 9expert · hard

   A team uses a generative AI tool to generate test data for a system that has strict data privacy requirements. The AI generates a dataset that is highly realistic but includes data that appears to be based on real individuals. The team is unsure if the data is synthetic or real. What is the best way to handle this situation?

   • ADiscard the dataset and generate new data with a prompt that explicitly requests synthetic data and includes a disclaimer
   • BUse the dataset but anonymize it by changing names and addresses
   • CRun a data validation tool to check if the dataset matches known real individuals, and if it does, discard it and regenerate with a synthetic-data prompt
   • DUse the dataset because it is generated by AI and therefore synthetic
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6. 10foundation · easy

   What is a typical input for generative AI to produce a test script?

   • AA natural language description of the test steps and expected results
   • BThe production database schema only
   • CThe list of known defects from previous releases
   • DThe final test execution report
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