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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

Question 1

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. Increase the 'max_tokens' parameter within the '!PREDICT' function options to accommodate longer document responses from the model.
B. 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.
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. Implement a pre-processing step to split documents exceeding 125 pages or 50 MB into smaller, compliant files before loading to the stage.

Question 2

A project team is preparing to deploy a Document AI solution to process scanned customer feedback forms. They have created a dedicated role, 'customer feedback _ processor', and successfully granted it the SNOWFLAKE. DOCUMENT_INTELLIGENCE_CREATOR database role. The environment consists of 'feedback database, 'forms schema' schema, and 'ai workload warehouse. However, when the attempts to prepare a Document AI model build in Snowsight, they encounter a 'permission denied' error. Which of the following missing 'USAGE' grants could be the direct cause of this error?

A.

B.

C.

D.

E.

Question 3

A data platform architect is evaluating the integration of 'SNOWFLAKE.CORTEX.TRANSLATE into several automated data pipelines. One pipeline involves real-time translation of messages for a chat application, while another is for batch processing of archived documents. The architect is considering various Snowflake features for orchestration and deployment. Which of the following considerations about 'SNOWFLAKE.CORTEX.TRANSLATE is accurate?

A. The Snowflake managed model used by the 'TRANSLATE' function has a context window of 4,096 tokens, meaning texts longer than this will be truncated before translation.
B. To manage potential failures in a production pipeline, 'SNOWFLAKCORTEX.TRANSLATE should be wrapped in 'TRY COMPLETE' for robust error handling, returning NULL on failure instead of an error.
C. The 'TRANSLATE' function can be seamlessly integrated into a dynamic table's ' SELECT statement to provide continuous, automated translation with minimal configuration.
D. When using 'TRANSLATE within a Snowpark Python User-Defined Function (UDF), the raw text data must be explicitly moved out of Snowflake's network boundary to the underlying LLM service for translation.
E. If 'TRANSLAT' is not natively available in the account's primary Snowflake region, cross-region inference cannot be enabled, thus preventing its use.

Question 4

A development team is constructing a Gen AI application using Snowflake Cortex LLM functions, particularly for conversational and text generation tasks. They are concerned about potential high costs due to token consumption. Which of the following strategies would most effectively help minimize token usage and optimize costs when working with these Cortex LLM functions?

A. Option E
B. Option A
C. Option C
D. Option D
E. Option B

Question 5

Considering Snowflake's Gen AI principles for cost governance within Snowflake Cortex, an ML engineer is assessing the expenditure for an LLM fine-tuning job. Which option correctly identifies how compute costs for Cortex Fine-tuning are primarily incurred and how fine-tuned models are treated regarding usage by other customers?

A. Costs are based on the number of fine-tuning jobs created, not tokens. Fine-tuned models are shared across all Snowflake customers to improve the general service.
B. Costs are incurred per hour of compute pool usage, similar to virtual warehouses. Fine-tuned models are anonymized and used to train future foundation models for all customers.
C. Fine-tuning costs are a flat monthly fee, irrespective of token usage or model size. Fine-tuned models become part of Snowflake's proprietary models after training.
D. Compute costs for fine-tuning are based on the number of tokens used in training, calculated as 'number of input tokens number of epochs trained'. Fine-tuned models built using a customer's data are available exclusively for that customer's use.
E. Only inference using fine-tuned models incurs costs, not the training itself. Fine-tuned models can be openly shared on the Snowflake Marketplace.

Solutions: