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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A financial analyst is concerned about the rising costs of their Document AI pipeline, which uses 'invoice_model!PREDlCT' to extract data from daily financial reports. They observe that their assigned 'LARGE virtual warehouse is running continuously, even during periods of low document ingestion, contributing significantly to their bill. They want to investigate how to reduce costs effectively for their existing Document AI setup.

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

2. A team is building a critical Document AI pipeline for continuous processing of new financial reports. They've identified that occasionally, the 'GET_PRESIGNED_URL' generated for specific documents expires before the '!PREDICT function can successfully process them, resulting in HTTP 403 errors. To mitigate this, they plan to modify their SQL query logic.
Which approach effectively addresses the presigned URL expiration issue without altering the stage definition or the model build itself, and adheres to recommended practices for handling batch processing as described in Snowflake's troubleshooting documentation?

A)

B)

C)

D)

3. A data scientist, 'Dl DEV', has been granted the


Despite these grants, 'DI_DEV' still receives a 'permission denied' error when attempting to ''prepare'' the Document AI model build in Snowsight. Which 'single missing privilege' is the most likely direct cause of this specific error for Document AI model build preparation?

A)

B)

C)

D)

E)

4.

A)

B)

C) The CREATE COMPUTE POOL privilege to provision resources for the Streamlit application.
D) The USAGE privilege on the specific database and schema where the Streamlit application and its underlying data tables are located.
E) The ACCOUNTADMIN role to ensure unrestricted access to all Snowflake Cortex features.

5. A team is developing a critical business intelligence application that leverages Snowflake Cortex Analyst to provide natural language querying capabilities over complex structured dat a. To minimize operational costs while maintaining high accuracy, which of the following strategies are most effective for optimizing the cost efficiency of the Cortex Analyst service?

A) Using a smaller, less capable LLM as the underlying summarization agent for multi-turn conversations to reduce token processing costs, even if it slightly degrades conversational context.
B) Configuring a custom instruction with a short, precise task description to reduce the input token count for the LLMs orchestrating SQL generation.
C) Leveraging Cortex Search Services integration within the semantic model to improve literal value matching, thereby reducing the need for Cortex Analyst to perform expensive fuzzy string matching or re-prompt the user.
D) Implementing a comprehensive Verified Query Repository (VQR) to guide Cortex Analyst towards pre-validated SQL queries for common questions, which ensures predictable execution and reduces LLM inference iterations.
E) Optimizing the semantic model YAML file by reducing the number of logical tables and columns to decrease the metadata processed by Cortex Analyst's LLMs per message.

Solutions: