Evolving Test Processes in AI-Enabled Test Organizations CT-GENAI Practice Questions (Page 5)

Questions 21–25

1. 21expert · hard

   A test team has integrated a generative AI tool that automatically generates and executes test cases. The tool has significantly reduced execution time, but the team has observed an increase in false-positive defect reports, which are consuming time to investigate. The team wants to maintain the efficiency gain while reducing the false-positive rate. What is the most effective approach?

   • ADisable the AI's defect reporting and rely on manual testers to identify all defects.
   • BRun the AI-generated tests twice and only report defects that appear in both runs.
   • CImplement a feedback loop where testers mark false positives, and use that data to fine-tune the AI's test generation and execution parameters.
   • DIncrease the AI's confidence threshold for reporting defects, accepting that some real defects may be missed.
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2. 22foundation · easy

   When adapting test planning for AI-driven testing, what should a test plan include?

   • AA requirement to use a specific AI vendor for all test generation
   • BA strategy for validating AI-generated test cases and data
   • CA guarantee that AI will find all critical defects
   • DA plan to eliminate all manual testing activities
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4. 23application · medium

   A test manager is revising the test plan for a project that will use generative AI to create and prioritize test cases from user stories. The organization has a strict data governance policy that prohibits using customer data in external AI services. The team has access to an internal, approved AI instance. What must the test plan include to align with governance while enabling the AI test design?

   • AA plan to use the external AI service but only for non-functional test cases, keeping functional cases internal.
   • BA schedule for periodic audits of the AI-generated test cases to ensure they meet coverage criteria.
   • CA requirement that all test cases generated by AI be manually rewritten by testers before execution.
   • DA data classification step that ensures only synthetic or anonymized data is fed to the AI, plus a verification that the internal instance is used.
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5. 24application · medium

   A QA team uses a generative AI model to create test cases from API specifications. The model produces many valid cases but also generates redundant scenarios that duplicate existing manual tests. The team wants to integrate AI into test design without bloating the suite. What is the most appropriate approach?

   • AAdd all AI-generated cases to the suite and rely on execution time to identify duplicates.
   • BAsk the AI model to generate fewer cases by lowering the temperature parameter.
   • CReplace all existing manual test cases with AI-generated ones to ensure consistency.
   • DUse a similarity analysis to deduplicate AI-generated cases against the existing suite before adding them.
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6. 25expert · hard

   A test manager is planning a new project that will use generative AI to generate test cases from user stories. The organization has a strict data residency requirement: all data must remain in the country where the project is located. The team has access to an internal AI instance and a cloud-based AI service hosted in another country. The cloud service is more capable and faster. What is the most appropriate planning decision?

   • AUse the cloud-based AI service and document the decision as a risk acceptance.
   • BUse the internal AI instance to ensure compliance with the data residency requirement, even though it is slower.
   • CUse the cloud-based AI service for test case generation and the internal instance for test data storage.
   • DUse the cloud-based AI service but ensure that all user stories are anonymized before sending them.
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