Option A is correct because AI Observability utilizes LLM judges (such as 'mistral-7b' or 'claude-3-5-sonnet') through 'COMPLETE (SNOWFLAKE.CORTEX)' function calls to compute evaluation metrics, and these calls incur charges based on the 'tokens processed'. Option D is correct as, beyond LLM judge costs, AI Observability also incurs warehouse charges for managing evaluation runs and for queries that compute evaluation metrics. Option E is correct because the view, with a filter for 'SERVICE _ TYPE ILIKE , provides a comprehensive daily credit usage report for all AI services, which would include AI Observability's components. Option B is incorrect; the view is specifically for Document AI processing functions like '!PREDICT and 'AI_EXTRACT , not for general LLM judge usage in AI Observability. The view is more appropriate for tracking individual Cortex function calls. Option C is incorrect because while prompt sizes might be similar, the pricing for different LLMs (e.g., 'mistral-7b' at 0.12 credits per million tokens vs. 'claude-3-5-sonnet' at 2.55 credits per million tokens for AI Complete) will still result in different billed amounts due to varying per-token costs, even if the number of tokens is the same.