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1. 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 B
C) Option A
D) Option D
E) Option E
2. A data engineer is tasked with defining a semantic model for Cortex Analyst to enable natural language queries over sales dat a. They are creating a YAML file to describe the logical structure. Which of the following statements correctly describe the configuration of this semantic model? (Select all that apply)
A) The 'VARIANT data type is a supported data type for columns defined as 'dimensions' or 'facts' within the semantic model, allowing flexible storage of complex attributes.
B) The 'tables' section must define logical tables that directly map to Snowflake physical tables or views, with 'base_table' specifying the fully qualified name (database, schema, table).
C) To ensure high accuracy for specific common questions, 'verified_querieS can be included, where the 'sqr field must reference the underlying physical table and column names (ee.g., not the logical names defined in the semantic model.
D) Dimensions such as 'product_category' can include a configuration, which can specify an optional 'literal_column' that, if omitted, defaults to the search index column.
E) A 'metric' like 'total_revenue' can be defined using an 'expr' that references other logical columns, such as 'sum(profity from a logical 'sales_data' table, within the semantic model.
3. A data engineering team is building a Retrieval Augmented Generation (RAG) pipeline that heavily relies on 'SNOWFLAKE.CORTEX.EMBED_TEXT 768' to process millions of documents daily. They need to optimize for both cost and retrieval quality. Which of the following statements are true regarding the cost and performance of 'EMBED_TEXT 768' in Snowflake? (Select all that apply)
A) The 'EMBED_TEXT 768' function is billed based on the number of 'output tokens' generated by the embedding model, as this represents the computational complexity of the vector.
B) To minimize costs for ' EMBED_TEXT 768 operations, it is recommended to execute queries using a smaller virtual warehouse (no larger than MEDIUM), as larger warehouses do not improve performance for these functions.
C) The 'snowflake-arctic-embed-m-vl .5 model, used by 'EMBED TEXT 768', has a context window of 512 tokens, and texts exceeding this length are truncated before embedding.
D) The 'EMBED TEXT 768' function, regardless of the 768-dimension model used, has a fixed cost of 1.50 Credits per one million Tokens processed.
E) For optimal retrieval quality in RAG scenarios, text should be split into chunks of no more than 512 tokens before being passed to 'EMBED TEXT 768', even if the model supports a larger context window.
4. A Snowflake user attempts to access the Cortex Playground to experiment with LLM functions but receives an error indicating insufficient privileges. They have an active Snowflake session. Which of the following steps must be taken to ensure they can successfully use the Cortex Playground?
A) Option C
B) Option B
C) Option A
D) Option D
E) Option E
5. A Gen AI Engineer is configuring a new semantic model for Cortex Analyst to process customer feedback. The goal is to ensure that when a user asks for sentiment analysis, the generated SQL queries always include an aggregation by a dimension and present the results as a percentage. The engineer plans to use custom instructions for this purpose. Which of the following details about is true and crucial for successful implementation?
A) Option C
B) Option B
C) Option A
D) Option D
E) Option E
Solutions:
| Question # 1 Answer: B,C,D,E | Question # 2 Answer: B,D,E | Question # 3 Answer: B,C,E | Question # 4 Answer: C,D | Question # 5 Answer: A |
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