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最新的 Snowflake Certification GES-C01 免費考試真題:

1. A data engineering manager needs to audit Cortex LLM function costs to identify specific SQL queries that are unexpectedly high in token consumption for the 'llama3.1-8b' model. They require granular analysis of prompt, completion, and guardrail token usage for these queries. Which of the following Snowflake methods or views would provide the necessary insights?

A) Option D
B) Option E
C) Option A
D) Option C
E) Option B

2. A Snowflake developer is tasked with enhancing a daily data pipeline. The pipeline processes raw text descriptions of system events and needs to extract structured information, specifically the (string) and its (string, restricted to 'low', 'medium', 'high', 'critical'). The output must be a strictly formatted JSON object, ensuring data quality for downstream analytics.
Consider the following SQL snippet intended for this transformation:

Which of the following statements are correct regarding this implementation and best practices for using with structured outputs in a data pipeline?

A) Setting 'temperature 'to '0.7 ' is optimal for ensuring the most consistent and deterministic JSON outputs, especially for complex extraction tasks.
B) For all models supported by 'AI_COMPLETE' Structured Outputs, the 'additionalPropertieS field must be set to 'false' in every node of the schema, and the 'required' field must contain all property names.
C) Using 'TRY COMPLETE instead of would allow the pipeline to gracefully handle cases where the model fails to generate a valid JSON response by returning 'NULL' instead of an error.
D) The 'response_format' correctly defines the expected JSON structure, using 'enum' for 'severity_lever and 'required' to ensure 'event_name' and severity_lever are always present if extracted.
E) The complexity of the JSON schema, particularly deep nesting, does not impact the number of tokens processed and billed for 'AI_COMPLETE Structured Outputs.

3. A data team is deploying a new customer service chatbot using Snowflake Cortex Analyst. To accurately forecast and optimize their costs, the team needs to understand how Cortex Analyst billing works. Which of the following statements accurately describe the cost considerations for Snowflake Cortex Analyst?

A) Cortex Analyst incurs compute costs based on the total number of input and output tokens processed in each conversation turn.
B) Only successful responses (HTTP 200) from Cortex Analyst are counted towards the credit usage.
C) Virtual warehouse compute is directly billed for Cortex Analyst operations, and its size can be adjusted to optimize query generation performance and cost.
D) Cortex Analyst's Cloud Services compute costs are subject to a daily adjustment, where Snowflake only bills if these costs exceed 10% of the daily virtual warehouse cost for the account.
E) Costs for Cortex Analyst are primarily driven by the number of successful messages processed, with a rate of 0.067 Credits per message.

4. An administrator is reviewing their Snowflake bill and observes higher than expected storage and cloud services compute costs for a newly deployed Cortex Search Service. They need to investigate these charges. Which of the following statements correctly explains how these specific costs are incurred or can be monitored for a Cortex Search Service?

A) The 'CORTEX_DOCUMENT_PROCESSING_USAGE_HISTORY view is the most appropriate tool to monitor Cortex Search storage and cloud services compute costs, as it tracks all ' Services usage.
B) Cloud Services compute costs for Cortex Search are always billed without any adjustments, regardless of the daily virtual warehouse compute costs, because they are considered serverless features.
C) High cloud services compute costs for Cortex Search are primarily driven by the complexity of the embedding model selected and can be optimized by choosing a simpler model.
D) The 'CORTEX_SEARCH_DAILY_USAGE_HISTORY view provides detailed breakdowns of storage costs per TB and cloud services compute credits incurred, including the 10% daily warehouse cost adjustment.
E) Storage costs are incurred for both the materialized source query data and the search index data structures, and these costs can be estimated by materializing the source query into a table using the CORTEX_SEARCH_DATA_SCAN table function, and then examining the size of that table.

5. An AI developer is building a Snowflake data pipeline to prepare unstructured data for a RAG application. The pipeline involves extracting text, splitting it into chunks, generating embeddings, and then indexing for Cortex Search. Considering the role of helper functions like SNOWFLAKE.CORTEX.SPLIT_TEXT_RECURSIVE_CHARACTER
, which of the following statements accurately describes its typical operational placement and interaction within this Gen AI pipeline?

A) The function's recursive nature enables it to automatically detect and correct factual inconsistencies or 'hallucinations' present in the original large text documents before they are embedded.
B) It is typically applied after an embedding function (e.g.,
C) It is a post-processing step for LLM-generated responses, used to break down long answers into digestible paragraphs for user display in chat interfaces.
D) It replaces the need for
E) Its output, consisting of smaller text chunks, serves as the direct input for text embedding functions that then convert these chunks into vector representations for semantic indexing.

問題與答案：