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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data engineer is designing an automated pipeline in Snowflake to process streaming customer support tickets using LLM functions. To ensure the pipeline is robust against unexpected LLM errors (e.g., due to malformed input or transient service issues), they decide to use SNOWFLAKE.CORTEX.TRY_COMPLETE instead of SNOWF LAKE .CORTEX.COMPLETE
. Which of the following is the most significant advantage of using
TRY_COMPLETE
in this scenario?

A) It provides a built-in retry mechanism for LLM calls that encounter temporary failures, automatically re-submitting the prompt up to three times.
B) It processes tokens at a significantly reduced rate compared to
C) It returns
D) It automatically adjusts the LLM's
E) It offers enhanced security features, such as automatic input sanitization, which are not available in the standard

2. A Streamlit application developer wants to use AI_COMPLETE (the latest version of COMPLETE (SNOWFLAKE.CORTEX)) to process customer feedback. The goal is to extract structured information, such as the customer's sentiment, product mentioned, and any specific issues, into a predictable JSON format for immediate database ingestion. Which configuration of the AI_COMPLETE function call is essential for achieving this structured output requirement?

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

3. A data engineering team is optimizing an AI-infused pipeline that processes millions of rows of customer interaction data in a LOG_DATA table using various Snowflake Cortex AI functions. They need to accurately estimate costs and ensure optimal performance. Which of the following statements regarding cost, performance, and operational considerations for these functions are true?

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

4. 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) 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.
B) Configuring a custom instruction with a short, precise task description to reduce the input token count for the LLMs orchestrating SQL generation.
C) 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.
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) 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.

5. An ML Engineer has successfully deployed a custom text embedding model, 'my_embedder model', to a Snowpark Container Service named 'text embedding_service' within their Snowflake account. This model has an 'encode' method that accepts a string and returns a vector. They now need to integrate inference calls from this deployed model into various applications. Which of the following are valid ways to invoke this model for inference?

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

Solutions: