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A data analytics team aims to enhance their understanding of customer feedback stored in a Snowflake table called CUSTOMER_FEEDBACK. This table has a REVIEW_TEXT column containing raw customer comments and a CUSTOMER_SEGMENT column. The team wants to classify each review into predefined categories and then generate a concise summary of all reviews for each customer segment. Which of the following Snowflake Cortex AI functions and approaches should they use?
A. Option B
B. Option A
C. Option E
D. Option D
E. Option C
Answer: B,C,E
Explanation:
NEW QUESTION # 103
An ML engineer is developing a RAG application in Python and wants to use the TruLens SDK to trace the distinct phases of its execution, specifically the context retrieval and answer generation steps. They aim to clearly differentiate the tracing of the function responsible for retrieving context.
A.
B.
C.
D.
E.
Answer: C
Explanation:
To instrument a function for context retrieval using the TruLens SDK and clearly differentiate its tracing, the decorator should be used with 'span_type=SpanAttributes.SpanType.RETRlEVAL'. This is directly demonstrated in the source for tracing a function with a specific span type. Option B uses a string literal for 'span_type' , which is not the correct way to reference the enum member. Option C uses 'SpanAttributes.SpanType.GENERATlON' , which is intended for LLM inference, not context retrieval. Option D uses the decorator without a specific 'span_type' , which would not clearly differentiate the context retrieval phase. Option E uses non- existent decorators and types(@trace_function', 'spanTypes').
NEW QUESTION # 104
A.
B.
C.
D. Data for all these operations remains within Snowflake's governance boundary.
E.
Answer: D
Explanation:
Option D is correct. All the mentioned Snowflake Cortex AI functions , , 'SNOWFLAKE.CORTEX.COMPLETE) are designed to operate within Snowflake's governance boundary under default configurations. uses Snowflake's proprietary Arctic-TILT model for document extraction, keeping data within the platform. Snowflake Cortex AI functions, including embedding and completion models like 'mistral-large? , are fully hosted and managed by Snowflake, ensuring data remains secure and in place. While REGION' allows processing in a different region, user inputs and outputs are not stored or cached, maintaining data within Snowflake's overall control. Therefore, no data egress to a third-party LLM provider occurs in these steps. Options A, B, and C are incorrect as they contradict the principle of Snowflake-hosted and managed AI features. Option E is incorrect because the 'CORTEX MODELS ALLOWLIST restricts which models can be used, but it does not dictate data egress, as the allowed models are still Snowflake-hosted.
NEW QUESTION # 105
An operations manager is tasked with monitoring the cost and ensuring compliance for a Cortex Analyst deployment that uses the REST API. They are particularly concerned with accurately tracking credit consumption and understanding the implications of enabling external models. Which of the following statements correctly describe aspects of Cortex Analyst cost and governance?
A. Cortex Analyst credit usage is based on the number of messages processed, at a rate of 67 Credits per 1 ,000 messages, and only successful responses (HTTP 200) are counted.
B. Credit consumption for Cortex Analyst is primarily based on the number of tokens processed by the underlying LLMs, with more complex natural language questions leading to higher token usage and costs.
C. The view can be queried to track detailed information about cortex Analyst requests, including generated SQL and any errors.
D. Enabling Azure OpenAI models via the ' ENABLE_CORTEX_ANALYST_MODEL_AZURE_OPENAF parameter ensures that all customer data and prompts remain within Snowflake's governance boundary and fully respect RBAC policies for those models.
E. Snowflake's view provides granular usage information for REST API requests to cortex Analyst, including tokens processed per model.
Answer: A
Explanation:
NEW QUESTION # 106
An operations team at a company is implementing a robust governance framework to monitor and optimize the costs associated with their Snowflake Cortex LLM function usage. They need to identify which functions are driving the highest token consumption and overall credit usage to pinpoint areas for cost reduction. Which of the following monitoring tools or methods are appropriate for gaining these insights into Cortex LLM function costs and token consumption?
A. Option C
B. Option A
C. Option E
D. Option B
E. Option D
Answer: B,C,D,E
Explanation:
Option A is correct because the 'SNOWFLAKE.ACCOUNT USAGE.CORTEX_FUNCTIONS_QUERY_USAGE_HISTORY view provides granular usage information, including 'prompt_tokens' and 'completion_tokens' , for individual Cortex LLM function calls and the models used. Option B is correct because the 'METERING DAILY HISTORY view, found in the 'ACCOUNT USAGE schema (or 'ORGANIZATION_USAGE' for organization-wide), can be filtered by 'SERVICE_TYPE = 'AI_SERVICES" to retrieve daily aggregated credit consumption for all AI services, including Cortex LLM functions. Option D is correct because when 'Cortex Guard' is enabled for 'COMPLETE calls, the response's 'usage' object includes a 'guard_tokens' field, which indicates the tokens consumed by the guardrail processing. Monitoring this helps understand its cost and identify prompts that frequently trigger it. Option E is correct because the view is used to monitor the cost associated with ' per input token, which is directly relevant to embedding LLM functions like 'EMBED TEXT 1024'. Option C is incorrect because the view specifically tracks Document AI processing functions (e.g., 'PREDICT , 'PARSE_DOCUMENT, SAI_EXTRACT) and reports pages processed, not SNOWFLAKE.CORTEX.COMPLETE function usage for multimodal inputs.
NEW QUESTION # 107
......
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