Snowflake GES-C01 actual dump : SnowPro® Specialty: Gen AI Certification Exam

An enterprise is deploying a new RAG application using Snowflake Cortex Search on a large dataset of customer support tickets. The operations team is concerned about managing compute costs and ensuring efficient index refreshes for the Cortex Search Service, which needs to be updated hourly. Which of the following considerations and configurations are relevant for optimizing cost and performance of the Cortex Search Service in this scenario?

• A. For optimal performance and cost efficiency, Snowflake recommends using a dedicated warehouse of size no larger than MEDIUM for each Cortex Search Service.
• B. For embedding text, selecting a model like
  
• C. CHANGE_TRACKING
• D. The primary cost driver for Cortex Search is the number of search queries executed against the service, with the volume of indexed data (GBImonth) having a minimal impact on overall billing.
• E. The

A data engineer is tasked with implementing a product recommendation system in Snowflake. They have pre-computed product embeddings and want to find similar items using VECTOR_COSINE_SIMILARITY They are evaluating options for interacting with this function. Which of the following statements is TRUE regarding the use of VECTOR_COSINE_SIMILARITY and Snowflake's VECTOR data type?

• A. A column defined as
  
• B.
  
• C. The
  
• D. Direct comparison operators like
  
• E. The maximum dimension supported by the
  

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. Administrators can monitor Cortex Analyst requests, including the user, question asked, generated SQL, and errors, by querying the SNOWFLAKLOCAL .CORTEX_ANALYST_REQUESTS function.
• B. The primary cost incurred for Cortex Analyst is based on the number of tokens processed by the underlying LLMs, with more complex natural language questions directly leading to higher token usage and charges.
• C. Snowflake strongly recommends enabling the ENABLE_CORTEX_ANALYST_MODEL_AZURE_OPENAI account parameter to leverage Azure OpenAI models for Cortex Analyst, as it offers the highest performance and respects RBAC restrictions for these models.
• D. When using Snowflake-hosted LLMs (e.g., from Mistral or Meta) with Cortex Analyst, all customer data, including metadata and prompts, remains within Snowflake's governance boundary.
• E. Semantic models for Cortex Analyst, whether stored as YAML files or native semantic views, should have their access controlled by RBAC. This implicitly controls access to the underlying tables referenced in the semantic model.

A financial analytics team is using AI_COMPLETE to extract specific financial metrics (e.g., revenue, profit margin) from quarterly reports and requires the output in a strict JSON format for automated ingestion into a data warehouse. They've encountered issues where the LLM sometimes generates malformed JSON or includes extraneous text. Which of the following approaches will help ensure deterministic, schema-compliant JSON outputs and mitigate these 'hallucinations' related to format?

• A. Set the 'temperature' option to 0 when calling 'AI_COMPLETE' to obtain the most consistent and deterministic JSON outputs.
• B. Include a prompt instruction such as 'Respond in JSON' to improve adherence, especially for complex tasks, even though 'response_format' is provided.
• C.
  
• D.
  
• E.
  

An operations team is investigating an issue with a generative AI application powered by Snowflake Cortex Analyst, where users reported unexpected behavior in generated SQL. To diagnose the problem, they examine the detailed event logs captured by Snowflake AI Observability. Which categories of information can they expect to find in these event tables to assist their investigation?

• A. Real-time CPU and memory usage statistics for the Snowflake virtual warehouse executing the LLM inference.
• B. The full text of the natural language questions submitted by the users.
• C. Any error messages or warnings that occurred during the processing of the request.
• D. The complete request and response bodies associated with the application's execution steps.
• E. The exact SQL queries that Cortex Analyst generated in response to user questions.