Option A is correct: Different embedding models have varying costs per million tokens. Switching from 'snowflake-arctic-embed-l- v2.0' (0.05 credits/M tokens) to 'snowflake-arctic-embed-m-v1 .5' (0.03 credits/M tokens) would directly reduce costs if the smaller model meets quality requirements. Option C is correct: The parameter controls how often the search service is refreshed. Increasing the 'TARGET_LAG' reduces the frequency of embedding jobs, directly decreasing 'EMBED_TEXT_TOKENS costs over, time. Option B is incorrect: 'EMBED_TEXT_TOKENS' costs are based on the total number of tokens processed. Splitting a 500-token row into smaller chunks still results in processing the same total number of tokens for that row, so it doesn't reduce the total 'EMBED_TEXT _ TOKENS' cost, although it can improve search quality. Option D is incorrect: costs are based on the volume of tokens, not processing speed. A larger warehouse size does not reduce the number of tokens and is not recommended for cost reduction for EMBED_TEXT_TOKENS'; Snowflake recommends a warehouse size no larger than MEDIUM for Cortex Search services. Option E is incorrect: The 'ATTRIBUTES' field primarily affects filtering capabilities, and the embedding cost is associated with the primary search column, not each individual attribute incurring a separate embedding cost.