LLMOps When Deploying and Managing LLMs for Software Testing CT-GENAI Practice Questions (Page 1)

Questions 1–5

1. 1foundation · easy

   What is the primary role of LLMOps in the context of software testing?

   • ATo manage the lifecycle of LLMs, from deployment to monitoring, specifically for testing activities
   • BTo automate the creation of test cases using natural language processing
   • CTo ensure LLMs are trained on testing data before they are used in production
   • DTo replace traditional test automation frameworks with LLM-based tools
   Reveal Solution Discussion Select an answer first
2. 2foundation · easy

   What is the purpose of validating LLM outputs against a golden dataset in a testing context?

   • ATo reduce the size of the training data
   • BTo increase the model's inference speed
   • CTo automatically deploy the model to production
   • DTo measure the model's performance on known correct answers
   Reveal Solution Discussion Select an answer first
4. 3foundation · easy

   What is a key compliance consideration when using an LLM API that processes personal data in testing?

   • AMaking the LLM outputs publicly available
   • BAvoiding any form of logging
   • CEnsuring the data is stored in a specific cloud region
   • DUsing the same API key for all team members
   Reveal Solution Discussion Select an answer first
5. 4foundation · easy

   Which activity is a core responsibility of LLMOps when managing LLMs for software testing?

   • AMonitoring model performance and detecting drift in test outputs
   • BWriting test cases manually for each new feature
   • CDesigning the user interface of the test management tool
   • DDefining the business requirements for the software under test
   Reveal Solution Discussion Select an answer first
6. 5expert · medium

   An LLM-based test data generator is integrated into the CI/CD pipeline. The team notices that the generated data sometimes fails to meet the required schema constraints, but the model's overall accuracy metric remains high. They need to detect these failures early. What should they implement?

   • AAdd a validation step in the pipeline that checks the generated data against the schema and fails the build if violations are found.
   • BIncrease the model's temperature to generate more diverse data.
   • CMonitor the model's loss on the training set.
   • DUse a different LLM provider.
   Reveal Solution Discussion Select an answer first