Hands-On Exercise:The Use of AI in Testing CT-AI Practice Questions (Page 4)

Questions 16–19

1. 16application · medium

   A test team is preparing a hands-on exercise to apply AI-based test case generation to a legacy web application. They have a CSV file of historical defect data and a Selenium test suite. The exercise requires the AI model to learn from the defect data to generate new test cases. Which setup step is most critical to ensure the exercise produces meaningful results?

   • AConvert the Selenium test suite into a single monolithic script to simplify the data input format.
   • BNormalize and split the historical defect data into training and validation sets before feeding it to the model.
   • CRemove all historical defect records that do not have a corresponding Selenium test case.
   • DEnsure the AI model has access to the production database to learn real-time user behavior.
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2. 17application · medium

   A team is setting up a hands-on exercise to use an AI model for API test case generation. The exercise requires the model to learn from the API's OpenAPI specification and generate test cases that cover various endpoints and parameters. What is the most important data preparation step for this exercise?

   • AConvert the OpenAPI specification into a PDF document for easier reading.
   • BManually write a set of test cases and use them to train the model instead of the specification.
   • CParse the OpenAPI specification to extract the endpoints, parameters, and data types.
   • DRemove all optional parameters from the specification to simplify the model's task.
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4. 18foundation · easy

   During a hands-on exercise, a tester applies a natural language processing (NLP) model to automatically generate test cases from user story descriptions. Which AI technique is being applied to the testing task?

   • AClustering
   • BAnomaly detection
   • CRegression analysis
   • DNatural language processing
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5. 19application · medium

   In a hands-on exercise, a team uses an AI model to predict which test cases are likely to fail in the next build. The model is trained on historical test results. After the build, the team compares the model's predictions with the actual test results. The model predicted 10 test cases would fail, but only 4 of those actually failed. The model also missed 3 test cases that did fail. What is the precision of the model?

   • A70%
   • B57%
   • C40%
   • D75%
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