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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data engineering team needs to establish an automated pipeline in Snowflake to continuously extract 'contract_id' and effective_date' from new PDF contract documents uploaded to an internal stage named They have a pre-trained Document AI model named 'contract_processor'. Which of the following sets of SQL commands correctly configures the necessary Snowflake objects for this automated processing pipeline, including handling file access and initial data loading?

A)

B)

C)

D)

E)

2. A data operations team is attempting to scale up their Document AI automated pipeline. They are using a Snowflake Task to process a large volume of daily scanned invoices and receipts, which are stored in an internal stage 'financial_docs stage'. The current processing involves documents that are frequently around 75 MB each, and often there are batches exceeding 1 ,200 documents in a single day. The pipeline is failing consistently. Which of the following factors could be contributing to the failures in this Document AI automated pipeline? (Select all that apply.)

A) The account role executing the task lacks the 'SNOWFLAKCORTEX USER database role, which is a prerequisite for Document AI functions.
B) The number of documents in a single daily batch (exceeding 1 ,200) surpasses Document AI's processing limit per query.
C) The was created without specifying 'ENCRYPTION = (TYPE = 'SNOWFLAKE SSE')'.
D) The Snowflake Task is configured as a 'SERVERLESS TASK, which is not supported by Document AI.
E) The individual document size (75 MB) exceeds the maximum supported file size for Document AI.

3.

A)

B)

C)

D)

E)

4. An organisation is deploying a Snowflake Cortex Agent to assist business users with data insights. To enable users to interact with this agent via the agent : run API, which of the following database roles or privileges must be granted to their account role?

A) CREATE EXTERNAL AGENT
B) The
C) The
D) The
E) The

5. 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) Configure the internal stage used for storing invoices with 'ENCRYPTION = (TYPE = 'SNOWFLAKE SSE')'.
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) Increase the 'max_tokens' parameter within the '!PREDICT' function options to accommodate longer document responses from the model.
D) Redefine extraction questions to be more generic and encompassing, reducing the number of distinct questions needed per document.
E) 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.

Solutions: