Option A is correct. Embedding models like

have a fixed context window of 512 tokens. Using COUNT_TOKENS allows pre-checking if text fits within this limit, preventing truncation that can occur when the input exceeds the context window. Option B is incorrect because COUNT_TOKENS incurs only compute cost to run the function and does not incur additional token-based costs that would add to the billing of subsequent LLM inference calls. Option C is correct. For best search results, Snowflake recommends splitting text into chunks of no more than 512 tokens. This practice generally leads to higher retrieval and downstream LLM response quality in RAG scenarios, and COUNT_TOKENS is a valuable tool for managing these chunk sizes. Option D is incorrect. While COUNT_TOKENS incurs compute cost (not token-based cost), the amount of compute would generally scale with the size of the input text it processes, making the cost not entirely independent of input length, although it's not billed on a per-token basis for its own operation. Option E is correct. The COUNT_TOKENS function is available in all regions for any model, though the models themselves may have specific regional availabilities for other functions.