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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A Gen AI Specialist needs to extract the 'invoice number' and 'total_amount' from a specific invoice PDF, 'invoice_001 .pdf, located in an internal stage named They want to use the default (latest) model build version for a model named 'invoice_processor'. Which SQL query correctly uses the '!PREDICT method to extract the required information, and what key fields would be present in the JSON output for a successful extraction of 'invoice_number' and 'total_amount'?

A)

B)

C)

D)

E)

2.

A) The CREATE COMPUTE POOL privilege to provision resources for the Streamlit application.
B) The USAGE privilege on the specific database and schema where the Streamlit application and its underlying data tables are located.
C)

D) The ACCOUNTADMIN role to ensure unrestricted access to all Snowflake Cortex features.
E)

3. A Snowflake administrator is tasked with monitoring and optimizing costs for various Gen AI applications leveraging Snowflake Cortex LLM functions. They need to generate a report detailing token consumption for individual API calls to identify high-usage patterns and specific models. Which of the following Snowflake account usage views or methods would provide the most granular insights into prompt, completion, and guardrail token usage for Cortex LLM function calls?

A) Option A
B) Option C
C) Option D
D) Option B
E) Option E

4. A business team using a Snowflake Cortex Analyst-powered chatbot reports that follow-up questions in multi-turn conversations are sometimes slow to process, impacting user experience. The development team wants to optimize for responsiveness while maintaining accuracy in SQL generation. Which of the following strategies directly addresses latency in multi-turn conversations within Cortex Analyst, considering its underlying mechanisms?

A) Switch the underlying text-to-SQL LLM to a smaller model, such as
B) Increase the warehouse size used for Cortex Analyst queries to 'Large' to accelerate LLM inference.
C) Rely on
D) Implement an explicit LLM summarization agent within the semantic model to condense conversation history before it's passed to subsequent LLM calls.
E) Configure the semantic model to reset the conversation context after every three turns to limit token count.

5. A financial data team is implementing a Snowflake Cortex AI solution to summarize regulatory documents using SNOWFLAKE.CORTEX.TRY_COMPLETE They aim for both cost efficiency and high reliability, especially when dealing with documents that might occasionally exceed model context limits or result in malformed output. Which of the following statements about the cost and operational behavior of TRY_COMPLETE are TRUE in this context? (Select all that apply)

A) Option A
B) Option C
C) Option D
D) Option B
E) Option E

Solutions: