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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?

3.1.1 An LLM generates acceptance criteria for an e-commerce checkout process, consistently focusing on credit card payments while underrepresenting digital wallets, despite both being equally important.Which defect does this most likely illustrate?

4.1.3 What is the main focus of the hands-on demonstration of an LLM-powered agent?

4.1.1 In the architecture of an LLM-powered test infrastructure, which component manages authentication, data retrieval, and prompt preparation?

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?