Prep and try our GES-C01 valid and latest training questions & answers

A data engineering team is building an automated pipeline in Snowflake to process customer reviews. They need to use AI_COMPLETE to extract specific details like product, sentiment, and issue type, and store them in a strictly defined JSON format for seamless downstream integration. They aim to maximize the accuracy of the structured output and manage potential model limitations. Which statements accurately reflect the best practices and characteristics when using AI_COMPLETE with structured outputs for this scenario?

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

An enterprise is deploying a Cortex Analyst application and needs to manage its cost, ensure data security, and understand its operational behavior within Snowflake. Which of the following statements are true regarding the deployment, cost, and security of Cortex Analyst and its semantic models?

• A. Cortex Analyst applications are fully managed, and by default, all data, including metadata and prompts, remain within Snowflake's governance boundary when using Snowflake-hosted LLMs from Mistral and Meta.
• B. Semantic models for Cortex Analyst, stored as YAML files in a Snowflake stage, should have their stage access controlled by RBAC to implicitly control access to the underlying tables referenced in the semantic model.
• C. Administrators can monitor Cortex Analyst requests, including the user, question asked, generated SQL, and errors, by querying the SNOWFLAK LOCAL. CORTEX ANALYST_REQUESTS function.
• D. The CORTEX_ANALYST_USER database role is sufficient for making requests to Cortex Analyst, and the cost incurred is solely based on the number of tokens processed by the underlying LLMs, not per message.
• E. Enabling the account parameter is the recommended approach for using Azure OpenAI models with Cortex Analyst to ensure the highest performance and adherence to RBAC restrictions.

A data scientist is leveraging various Snowflake Cortex LLM functions to process extensive text data for an application. To effectively manage their budget, they need a clear understanding of how costs are incurred for each specific function. Which of the following statements accurately describe how costs are calculated for Snowflake Cortex LLM functions, with a particular focus on token usage?

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

A business intelligence team wants to enable non-technical users to query structured data in Snowflake using natural language. They are considering Cortex Analyst. What is the primary role of a semantic model in Cortex Analyst to achieve this goal for structured/text-to-SQL use cases?

• A. It serves as a cache for frequently requested data, reducing latency for natural language queries by providing pre-computed results.
• B. The semantic model provides a mapping between business-friendly terms and the underlying technical database schema, enhancing the LLM's ability to generate accurate SQL from natural language questions.
• C. It stores user authentication credentials and data access policies, ensuring that only authorized users can interact with the data.
• D. The semantic model directly executes SQL queries provided by end-users, bypassing the need for an LLM to generate them.
• E. The semantic model acts as a vector store, storing embeddings of all data columns to enable semantic search for natural language queries.

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 offers enhanced security features, such as automatic input sanitization, which are not available in the standard
• C. It returns
• D. It processes tokens at a significantly reduced rate compared to
• E. It automatically adjusts the LLM's