Essential Skills and Knowledge for Testing with Generative AI CT-GENAI Practice Questions (Page 3)

Questions 11–15

1. 11foundation · easy

   What is a common use of generative AI in defect analysis?

   • AAutomatically deploying fixes to production without human approval.
   • BReplacing the need for any manual defect triage.
   • CSummarizing large volumes of test logs to highlight potential defect patterns.
   • DGuaranteeing that all defects are found before release.
   Reveal Solution Discussion Select an answer first
2. 12foundation · easy

   Which mitigation strategy is effective for addressing the limitation of AI-generated test data that may not cover edge cases?

   • ADeleting all test data and relying on production data.
   • BCombining AI-generated data with manually crafted boundary and edge-case data.
   • CIncreasing the AI model's temperature to generate more random data.
   • DUsing only AI-generated data and ignoring edge cases.
   Reveal Solution Discussion Select an answer first
4. 13expert · hard

   A test team uses a generative AI model to automatically generate test cases for a new feature. The model produces a large number of test cases, but the team has limited time to execute them. They want to ensure the most important tests are run first. What is the best approach?

   • AUse a risk-based prioritization approach, where testers review the AI-generated cases and rank them by business risk.
   • BRun the test cases in the order they were generated by the AI.
   • CRun all test cases randomly to avoid bias.
   • DUse a second AI model to prioritize the test cases automatically.
   Reveal Solution Discussion Select an answer first
5. 14foundation · easy

   What is a primary advantage of using generative AI to create test data?

   • AIt guarantees that all generated data is free from any errors or inconsistencies.
   • BIt can produce large volumes of realistic data without exposing sensitive production information.
   • CIt ensures that test data is identical to production data in every way.
   • DIt eliminates the need for any data validation in the testing process.
   Reveal Solution Discussion Select an answer first
6. 15foundation · easy

   How can generative AI assist in detecting anomalies in test results?

   • ABy automatically fixing any defects found in the test results.
   • BBy learning patterns from historical test data and flagging results that deviate from those patterns.
   • CBy generating test results without executing any tests.
   • DBy ignoring all test failures and focusing only on passed tests.
   Reveal Solution Discussion Select an answer first