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NEW QUESTION # 263
A Snowflake administrator is tasked with monitoring and optimizing costs for various Gen AI applications leveraging Snowflake Cortex LLM functions. They need to generate a report detailing token consumption for individual API calls to identify high-usage patterns and specific models. Which of the following Snowflake account usage views or methods would provide the most granular insights into prompt, completion, and guardrail token usage for Cortex LLM function calls?
Answer: A,D
Explanation:
NEW QUESTION # 264
An organisation is deploying a Snowflake Cortex Agent to assist business users with data insights. To enable users to interact with this agent via the agent : run API, which of the following database roles or privileges must be granted to their account role?
Answer: E
Explanation:
Option A is incorrect. The
CREATE EXTERNAL AGENT
privilege is listed as a requirement for AI Observability to create and execute runs, not for direct interaction with a Cortex Agent via its API.
Option B is correct. To make a request to Cortex Agent via the
agent : run
API, a role must have either the
or
database role granted. The
role specifically provides access to the Agents feature. Option C is incorrect. EXECUTE TASK global privilege is a prerequisite for AI Observability setup, but not explicitly for simply invoking the Cortex Agent's agent : run API. Option D is incorrect. The
database role is required for Document AI, which an agent *might* leverage as a tool, but it's not a direct requirement for the role interacting with the agent API. Option E is incorrect. The sources specify that roles with
are sufficient for Cortex Agent API requests, indicating that the ACCOUNTADMIN role is not strictly necessary for this operation.
NEW QUESTION # 265
A company wants to ingest and process scanned invoices and digitally-born contracts in Snowflake. They need to extract all text, preserving layout for contracts and just the text content for scanned invoices. Which AI_PARSE_DOCUMENT modes would be most appropriate for this scenario, and what is the primary purpose of the function itself?
Answer: A
Explanation:
Option C is correct. AI_PARSE_DOCUMENT is a Cortex AI SQL function designed to extract text, data, and layout elements from documents with high fidelity, preserving structure like tables, headers, and reading order. For digitally-born contracts where layout preservation is needed, the mode is appropriate. For scanned invoices where only text content is needed without layout, the OCR mode, which extracts text LAYOUT from scanned documents and does not preserve layout, is suitable.
NEW QUESTION # 266
An ML Engineer has developed a custom PyTorch model for GPU-powered inference and successfully built an OCI-compliant image locally. They now need to push this image to a Snowflake image repository and configure a Snowpark Container Service to use it. The Snowflake account identifier is my org_name_my_account_id_prod. Which set of commands correctly demonstrates tagging the local image and pushing it to the repository?
Answer: D,E
Explanation:
NEW QUESTION # 267
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?
Answer: A,B,D,E
Explanation:
Option A is correct because a Cortex Search Service requires a virtual warehouse to refresh the service, which runs queries against base objects when they are initialized and refreshed, incurring compute costs. Option B is correct because the cost of embedding models varies. For example, 'snowflake-arctic-embed-m-v1.5 costs 0.03 credits per million tokens, while 'voyage-multilingual-2 costs 0.07 credits per million tokens. Choosing a more cost-effective model like 'snowflake-arctic-embed-m-v1.5 for English-only data can reduce token costs. Option C is correct because Snowflake recommends using a dedicated warehouse of size no larger than MEDIUM for each Cortex Search Service to achieve optimal performance. Option D is correct because change tracking is required for the Cortex Search Service to be able to detect and process updates to the base table, enabling incremental refreshes that are more efficient than full re-indexing. Option E is incorrect because Cortex Search Services incur costs based on virtual warehouse compute for refreshes, "EMBED_TEXT TOKENS' cost per input token, and a charge of 6.3 Credits per GB/mo of indexed data. The volume of indexed data has a significant impact, not minimal.
NEW QUESTION # 268
......
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