Exam GES-C01 Topic 1 Question 149 Discussion

Option B is correct: Cortex Search is explicitly designed as a RAG engine to enhance LLM responses with contextualized information from Snowflake data, which directly addresses factual accuracy and reduces hallucinations. Option C is correct: AI Observability's evaluation features, including 'factual correctness' and 'groundedness' scores, are specifically mentioned for detecting the truthfulness and relevance of responses based on retrieved context, and for avoiding LLMs with high hallucination frequencies, especially in summarization tasks. Option D is correct: The Cortex Analyst Verified Query Repository (VQR) provides a collection of pre-verified SQL queries for specific natural language questions, significantly improving the accuracy and trustworthiness of SQL generation and reducing errors that could be seen as 'hallucinations' in the text-to-SQL context. Option A is incorrect: While fine-tuning (using 'SNOWFLAKE.CORTEX.FINETUNE) can adapt a model to specific tasks and data, it is not a direct guarantee against 'all' factual inaccuracies or 'hallucinations' for 'any' LLM task, especially if the fine- tuning data itself is limited or the model generalizes poorly. RAG is generally preferred for grounding responses in up-to-date external knowledge. Option E is incorrect: Cortex Guard is designed to filter 'harmful or unsafe' LLM responses, not to directly ensure factual correctness or prevent hallucinations related to content accuracy or grounding.