Option A is correct because adding label descriptions and examples can improve classification accuracy, especially when category definitions are ambiguous. The source explicitly states that each label, description, and example counts as input tokens for each record processed by a 'CLASSIFY _ TEXT function call, incurring costs accordingly. Option B is correct because adding a clear 'task_description' can improve accuracy when the relationship between the input text and categories is ambiguous or nuanced. Option C is incorrect; while token counts contribute to cost, the sources do not recommend removing stop words and punctuation for cost reduction or as a general best practice for SCLASSIFY TEXT. The focus is on using plain English input. Option D is incorrect because the 'temperature' option is available for 'COMPLETE and functions to control output randomness, but it is not listed as an option for 'CLASSIFY _ TEXT in its syntax. Furthermore, while a lower temperature can make results more deterministic, the source does not link it to 'cheaper' inference cost for these task-specific functions, but rather to consistency for 'COMPLETE. Option E is incorrect because 'AI_CLASSIFY labels, descriptions, and examples are indeed counted as input tokens for 'each record processed' , not just once per call, as clearly stated in the cost considerations.