GES-C01 Exam Dumps | A data processing team is using Snowflake Document AI to extract data from incoming supplier invoices.

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Snowflake.GES-C01.v2025-10-06.q171
Question 136

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Question 136/171

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. 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.

B. Implement a pre-processing step to split documents exceeding 125 pages or 50 MB into smaller, compliant files before loading to the stage.

C. Redefine extraction questions to be more generic and encompassing, reducing the number of distinct questions needed per document.

D. Configure the internal stage used for storing invoices with 'ENCRYPTION = (TYPE = 'SNOWFLAKE SSE')'.

E. Increase the 'max_tokens' parameter within the '!PREDICT' function options to accommodate longer document responses from the model.

Correct Answer: B,D

The error messages 'Document has too many pages. Actual: 130. Maximum: 125.' and File exceeds maximum size. Actual: 54096026 bytes. Maximum: 50000000 bytes: directly indicate that the documents do not meet Document AI's input requirements, which specify a maximum of 125 pages and 50 MB file size. Therefore, implementing a pre-processing step to split or resize these documents is an essential solution (Option B). The error 'cannot identify image file < io.ByteslO object at Ox...>' is a known issue that occurs when an internal stage used for Document AI is not configured with encryption. Correctly configuring the stage with this encryption type is crucial for resolving this processing error (Option D). Option A, while addressing cost optimization, is not a root cause of the 'processing errors' themselves, although it is a best practice for cost governance as larger warehouses do not increase Document AI query processing speed. Option C is incorrect; best practices for question optimization suggest being specific, not generic. Option E is incorrect as 'max_tokenS relates to the length of the model's output, not the input document's size or page limits.

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