A data processing team is using Snowflake Document AI to extract data from incoming supplier invoices. They observe that many documents are failing to process, and successful extractions are taking longer than expected, leading to increased costs. Upon investigation, they find error messages such as

. Additionally, their 'X-LARGE virtual warehouse is constantly active, contributing to higher-than-anticipated bills. Which two of the following actions are essential steps to troubleshoot and address the root causes of these processing errors and optimize their Document AI pipeline?
- A. Implement a pre-processing step to split documents exceeding 125 pages or 50 MB into smaller, compliant files before loading to the stage.
- B. Redefine extraction questions to be more generic and encompassing, reducing the number of distinct questions needed per document.
- C. Configure the internal stage used for storing invoices with 'ENCRYPTION = (TYPE = 'SNOWFLAKE SSE')'.
- D. Scale down the virtual warehouse to 'X-SMALL' or 'SMALL' size, as larger warehouses do not increase Document AI query processing speed and incur unnecessary costs.
- E. Increase the 'max_tokens' parameter within the '!PREDICT' function options to accommodate longer document responses from the model.
Reveal Solution
Discussion
Correct Answer: A,C 🗳️
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