Using AI to Analyze Reported Defects CT-AI Practice Questions (Page 4)

Questions 16–20

1. 16foundation · easy

   What is the main challenge that NLP helps overcome when analyzing defect reports?

   • ADefect reports are always stored in a structured database.
   • BDefect reports are never written in English.
   • CDefect reports are often written in unstructured, free-form text.
   • DDefect reports cannot be read by humans.
   Reveal Solution Discussion Select an answer first
2. 17foundation · easy

   What is the purpose of using clustering methods on defect reports?

   • ATo delete duplicate defect reports automatically
   • BTo assign a unique ID to each defect report
   • CTo translate defect reports into different languages
   • DTo group similar defects together to identify recurring patterns
   Reveal Solution Discussion Select an answer first
4. 18foundation · easy

   What is a key benefit of integrating AI defect analysis into existing defect management tools?

   • AIt requires testers to learn a completely new tool for every project.
   • BIt eliminates the need for any human involvement in defect management.
   • CIt allows testers to work within their familiar workflow while gaining AI insights.
   • DIt automatically fixes all defects without any manual effort.
   Reveal Solution Discussion Select an answer first
5. 19application · medium

   A development team uses an AI-based defect triage system. The system must automatically assign priority to new defects based on the affected module, the number of users impacted, and the defect's description. The team wants to ensure that defects in the payment module are always prioritized over defects in the reporting module, regardless of other factors. What should the team configure?

   • AAdjust the model's confidence threshold so that only high-confidence predictions are accepted.
   • BRetrain the AI model with more historical data that includes payment module defects.
   • CUse a separate AI model for payment module defects and another for reporting module defects.
   • DAdd a business rule that overrides the AI's priority when the affected module is the payment module.
   Reveal Solution Discussion Select an answer first
6. 20application · medium

   A QA team receives defect reports in multiple languages. They want to automatically extract the core issue from each report and translate it into a standardized English summary for analysis. Which combination of AI techniques should they use?

   • AUse clustering to group reports by language and then manually translate the largest cluster.
   • BUse sentiment analysis to determine the urgency of each report and prioritize accordingly.
   • CUse machine translation to convert reports to English, then apply NLP summarization to extract the core issue.
   • DUse a predictive model to classify reports by severity without translating them.
   Reveal Solution Discussion Select an answer first