1.1.4 A test engineer needs to identify mismatches between the expected UI layout described in user stories and screenshots from the latest build. They want to maximize test coverage by also generating additional test cases using the same model. Which approach is most suitable?

4.2.1 During fine-tuning of an LLM for test generation, the training dataset contains inconsistencies: some user stories are incomplete, and some test cases do not match the described functionality. After fine-tuning, the model frequently generates irrelevant or biased test cases. Which fine-tuning challenge does this illustrate?

3.4.1 A QA lead is tasked with ensuring that their GenAI testing pipeline follows best practices for data quality, transparency, and fault tolerance. Which standard directly addresses these needs?

1.1.3 A test automation team integrates an LLM into their defect triaging workflow. The initial implementation uses a model pre-trained on vast text, code, and image datasets without any fine-tuning. The team notices that while the model can categorize defects across multiple domains, it often fails to follow precise categorization rules provided in the prompt. Which type of LLM is most likely being used?

4.1.1 Which statement best describes why the architecture goes beyond a traditional client-server model?