Try Snowflake : GES-C01 practice torrent pass for sure

A Gen AI Specialist is setting up AI Observability for a new generative AI application in Snowflake to monitor its performance and debug issues. The application is built in Python. Which of the following prerequisites must be met to enable tracing for this application?

• A. The Python project requires installation of trulens-core and trulens-connectors-snowflake packages, with versions earlier than 2.1.2.
• B. The role used for creating and executing runs must have the CREATE EXTERNAL AGENT privilege on the schema.
• C. The account role used must be granted the SNOWFLAKE. CORTEX_USER database role.
• D. The Python environment must have the TRULENS_OTEL_TRACING environment variable set to o before connecting to Snowflake.
• E. The application must run exclusively within a Snowflake Notebook for observability features to function.

• A.
  
• B. Data for all these operations remains within Snowflake's governance boundary.
• C.
  
• D.
  
• E.
  

A developer is building a real-time chat application and wants to integrate a Large Language Model (LLM) hosted in Snowflake Cortex using its REST API. They need to send user prompts and receive streaming responses, ensuring secure authentication. Which of the following statements about using the Cortex REST API for the COMPLETE function are correct?

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

A security administrator is implementing strict model access controls for Snowflake Cortex LLM functions, including those accessed via the Cortex REST API. By default, the 'SNOWFLAKE.CORTEX USER' database role is granted to the 'PUBLIC' role, allowing all users to call Cortex AI functions. To enforce a more restrictive access policy, the administrator revokes 'SNOWFLAKE.CORTEX USER from 'PUBLIC'. Which of the following actions must the administrator take to ensure specific roles can 'still' make Cortex REST API requests, and what are the implications?

• A. The 'SNOWFLAKE.CORTEX USER database role must be granted to the specific account roles, and then these account roles must be granted to users. Additionally, the account parameter can be used to restrict which models are accessible.
• B. Access for Cortex REST API is managed independently of database roles; a separate REST API key must be provisioned for each user or application.
• C. The 'SNOWFLAKE.CORTEX USER database role must be granted directly to individual users who need access, as it cannot be granted to other account roles.
• D. Only the role can make cortex REST API calls after revoking 'SNOWFLAKE.CORTEX_USER from 'PUBLIC', as this role inherently bypasses all other access controls.
• E. The from 'SNOWFLAKCORTEX USER database role is only required for SQL functions, not for the Cortex REST API, so no further action is needed after revoking 'PUBLIC for REST API access.

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. 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.
• B. 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.
• C. Administrators can monitor Cortex Analyst requests, including the user, question asked, generated SQL, and errors, by querying the SNOWFLAKLOCAL .CORTEX_ANALYST_REQUESTS function.
• D. 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.
• E. 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.