Snowflake GES-C01

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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

Question #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.

Question #2
A Gen AI developer is deploying a customer support chatbot using 'SNOWFLAKE.CORTEX.COMPLETE for generating responses. To ensure the chatbot does not provide inappropriate or harmful content, they have enabled Cortex Guard. A specific user prompt is now causing the LLM to attempt generating content that violates the defined safety policies. What is the expected outcome of the 'COMPLETE' function call in this scenario, and how is it typically communicated to the application?

A. The 'COMPLETE function will return a NULL value in the response, signifying that no safe response could be generated, similar to error handling.
B. The 'COMPLETE function will raise a SQL exception, indicating a 'PROHIBITED_CONTENT error, which the calling application must handle with a 'TRY...CATCH' block.
C. The LLM, through Cortex Guard's internal mechanisms, will automatically rephrase or redact the harmful parts of its response to comply with safety guidelines before returning it.
D. The Snowflake account will be temporarily locked for violating the acceptable use policy, and all subsequent LLM calls will fail until manual intervention.
E. The 'COMPLETE function will return a generic, pre-defined filtered message, such as 'Response filtered by Cortex Guard', in place of the harmful content.

Question #3
A data engineering team is setting up an automated pipeline to extract information from new invoices using Document AI. They've created a database and schema Cinvoice_db.invoice_schema') and a Document AI model build They then created an internal stage for documents. When they attempt to run the method on documents uploaded to 'invoice_stage' , they consistently receive the following error:

Given this error message, which 'corrective SQL command' addresses the most likely misconfiguration of the 'invoice_stage' to allow Document AI processing?

A.

B.

C.

D.

E.

Question #4
A business user frequently asks Cortex Analyst questions that require filtering on specific product names, such as "What were the sales for 'iced tea' last month?" The 'product' dimension has many distinct values (high cardinality), and Cortex Analyst sometimes struggles to accurately identify the exact literal product name, leading to less precise SQL queries. The Gen AI Specialist wants to enhance Cortex Analyst's ability to find these literal values for the 'product' dimension. To improve Cortex Analyst's literal search capability for the high-cardinality 'product' dimension, which of the following is the most appropriate and recommended approach to configure in the semantic model?

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

Question #5
An organization enforces strict LLM access control. The has configured 'CORTEX MODELS_ALLOWLIST ='mistral-large2" and executed 'CALL SNOWFLAKE.MODELS.CORTEX BASE MODELS REFRESH()'. A developer, whose role DEV ROLE has been granted 'SNOWFLAKE.CORTEX USER' and 'SNOWFLAKE."CORTEX-MODEL-ROLE-CLAUDE-3-5- SONNET"' , attempts to make a REST API call to 'api/v2/cortex/inference:complete' using 'claude-3-5-sonnet' as the model name in the request body. Which of the following statements are true regarding this scenario?

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

Solutions: