Using AI to Analyze Reported Defects CT-AI Practice Questions (Page 1)

Questions 1–5

1. 1application · medium

   A QA team notices that many defect reports mention 'timeout' and 'slow response' in the same module. They want to automatically analyze these reports to identify the common root cause and suggest a fix. Which AI capability should they use?

   • AUse NLP to extract key phrases and correlate them with code changes to identify likely root causes.
   • BUse a clustering algorithm to group all defects in the module, regardless of symptoms.
   • CUse a rule-based system that flags any defect containing the word 'timeout' as a performance issue.
   • DUse a predictive model to forecast when the next timeout defect will occur.
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2. 2foundation · easy

   Which data source is most important for training a predictive defect model?

   • AHistorical defect data
   • BCurrent weather data
   • CMarketing campaign data
   • DEmployee salary data
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4. 3foundation · easy

   Which of the following is an example of integrating AI into a defect management workflow?

   • ATesters manually copy defect data into a separate AI tool for analysis.
   • BThe defect tracking tool is replaced by a completely new AI-based system.
   • CAI is used only in a separate research project, not in daily defect management.
   • DAn AI model automatically categorizes new defects and suggests a priority when they are entered into the tracking system.
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5. 4application · medium

   A company uses an AI-based defect analysis tool that clusters similar defects and suggests priorities. The QA manager wants to ensure that the tool's suggestions are reviewed by a human before being applied to the defect tracking system. What is the best way to integrate this human-in-the-loop step?

   • AAllow the tool to directly update the defect tracking system and rely on the QA team to review the changes later.
   • BDisable the AI tool and have the QA team manually cluster and prioritize all defects.
   • CUse the AI tool only for reporting and not for any updates to the defect tracking system.
   • DConfigure the tool to create a draft priority and cluster assignment, and require a QA analyst to approve before the changes are saved.
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6. 5foundation · easy

   What is the goal of predictive defect analysis?

   • ATo generate test data for all possible inputs
   • BTo automatically fix all known defects
   • CTo predict which areas of the software are most likely to have future defects
   • DTo replace the need for manual testing entirely
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